The diagnostic power of motor unit potential analysis: an objective bayesian approach

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.

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.

A Density-Based Clustering Approach to Motor Unit Potential Characterizations to Support Diagnosis of Neuromuscular Disorders

Electrophysiological muscle classification involves characterization of extracted motor unit potentials (MUPs) followed by the aggregation of these MUP characterizations. Existing techniques consider three classes (i.e., myopathic, neurogenic, and normal) for both MUP characterization and electrophysiological muscle classification. However, diseased-induced MUP changes are continuous in nature, which make it difficult to find distinct boundaries between normal, myopathic, and neurogenic MUPs. Hence, MUP characterization based on more than three classes is better able to represent the various effects of disease. Here, a novel, electrophysio- logical muscle classification system is proposed, which considers a dynamic number of classes for characterizing MUPs. To this end, a clustering algorithm called neighbor- hood distances entropy consistency is proposed to find clusters with arbitrary shapes and densities in an MUP feature space. These clusters represent several concepts of MUP normality and abnormality and are used for MUP characterization instead of the conventional three classes. An examined muscle is then classified by embedding its MUP characterizations in a feature vector fed to an ensemble of support vector machine and nearest neighbor classifiers. For 103 sets of MUPs recorded in tibialis anterior muscles, the proposed system had a 97% electro-physiological muscle classification accuracy, which is significantly higher than in previous works.

Aggregate features in multisample classification problems

This paper evaluates the classification of multisample problems, such as electromyographic (EMG) data, by making aggregate features available to a per-sample classifier. It is found that the accuracy of this approach is superior to that of traditional methods such as majority vote for this problem. The classification improvements of this method, in conjunction with a confidence measure expressing the per-sample probability of classification failure (i.e., a hazard function) is described and measured. Results are expected to be of interest in clinical decision support system development.

Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography

BACKGROUND

Methods for the calculation and application of quantitative electromyographic (EMG) statistics for the characterization of EMG data detected from forearm muscles of individuals with and without pain associated with repetitive strain injury are presented.

METHODS

A classification procedure using a multi-stage application of Bayesian inference is presented that characterizes a set of motor unit potentials acquired using needle electromyography. The utility of this technique in characterizing EMG data obtained from both normal individuals and those presenting with symptoms of "non-specific arm pain" is explored and validated. The efficacy of the Bayesian technique is compared with simple voting methods.

RESULTS

The aggregate Bayesian classifier presented is found to perform with accuracy equivalent to that of majority voting on the test data, with an overall accuracy greater than 0.85. Theoretical foundations of the technique are discussed, and are related to the observations found.

CONCLUSIONS

Aggregation of motor unit potential conditional probability distributions estimated using quantitative electromyographic analysis, may be successfully used to perform electrodiagnostic characterization of "non-specific arm pain." It is expected that these techniques will also be able to be applied to other types of electrodiagnostic data.

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.

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.

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.

Motor unit potential characterization using “pattern discovery”

Typically in clinical practice, electromyographers use qualitative auditory and visual analysis of electromyographic (EMG) signals to help infer if a neuromuscular disorder is present and if it is neuropathic or myopathic. Quantitative EMG methods exist that can more accurately measure feature values but require qualitative interpretation of a large number of statistics. Electrophysiological characterization of a neuromuscular system can be improved through the quantitative interpretation of EMG statistics. The aim of the present study was to compare the accuracy of pattern discovery (PD) characterization of motor unit potentials (MUPs) to other classifiers commonly used in the medical field. In addition, a demonstration of PD's transparency is provided. The transparency of PD characterization is a result of observing statistically significant events known as patterns. Using clinical MUP data from normal subjects and patients with known neuropathic disorders, PD achieved an error rate of 30.3% versus 29.8% for a Naïve Bayes classifier, 30.1% for a Decision Tree and 29% for discriminant analysis. Similar results were found for simulated EMG data. PD characterization succeeded in interpreting the information extracted from MUPs and transforming it into knowledge that is consistent with the literature and that can be valuable for the capture and transparent expression of clinically useful knowledge.

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.

The role of different EMG methods in evaluating myopathy

For the diagnosis of myopathy, EMG may have an important role along with blood tests, muscle biopsies and genetic testing. This review evaluates different EMG methods in the diagnosis of myopathy. These include manual analysis of individual motor unit potentials and multi-motor unit potential analysis sampled at weak effort. At high effort, turns-amplitude analyses such as the cloud analysis and the peak ratio analysis have a high diagnostic yield. The EMG can seldom be used to differentiate between different types of myopathy. In the channelopathies, myotonia, exercise test and cooling of the muscle are helpful. Macro-EMG, single-fibre EMG and muscle fibre conduction velocity analysis have a limited role in myopathy, but provide information about the changes seen. Analysis of the firing rate of motor units, power spectrum analysis, as well as multichannel surface EMG may have diagnostic potential in the future. EMG is of great importance in the diagnosing of patients with myopathy, preferably a needle electrode and quantitative analyses should be used. A combination of a method at weak effort as well as a method at stronger effort seems optimal.