JoVE Visualize What is visualize?
Stop Reading. Start Watching.
Advanced Search
Stop Reading. Start Watching.
Regular Search
Find video protocols related to scientific articles indexed in Pubmed.
Effects of aging on genioglossus motor units in humans.
PLoS ONE
PUBLISHED: 08-11-2014
Show Abstract
Hide Abstract
The genioglossus is a major upper airway dilator muscle thought to be important in obstructive sleep apnea pathogenesis. Aging is a risk factor for obstructive sleep apnea although the mechanisms are unclear and the effects of aging on motor unit remodeled in the genioglossus remains unknown. To assess possible changes associated with aging we compared quantitative parameters related to motor unit potential morphology derived from EMG signals in a sample of older (n?=?11; >55 years) versus younger (n?=?29; <55 years) adults. All data were recorded during quiet breathing with the subjects awake. Diagnostic sleep studies (Apnea Hypopnea Index) confirmed the presence or absence of obstructive sleep apnea. Genioglossus EMG signals were analyzed offline by automated software (DQEMG), which estimated a MUP template from each extracted motor unit potential train (MUPT) for both the selective concentric needle and concentric needle macro (CNMACRO) recorded EMG signals. 2074 MUPTs from 40 subjects (mean±95% CI; older AHI 19.6±9.9 events/hr versus younger AHI 30.1±6.1 events/hr) were extracted. MUPs detected in older adults were 32% longer in duration (14.7±0.5 ms versus 11.1±0.2 ms; P ?=? 0.05), with similar amplitudes (395.2±25.1 µV versus 394.6±13.7 µV). Amplitudes of CNMACRO MUPs detected in older adults were larger by 22% (62.7±6.5 µV versus 51.3±3.0 µV; P<0.05), with areas 24% larger (160.6±18.6 µV.ms versus 130.0±7.4 µV.ms; P<0.05) than those detected in younger adults. These results confirm that remodeled motor units are present in the genioglossus muscle of individuals above 55 years, which may have implications for OSA pathogenesis and aging related upper airway collapsibility.
Related JoVE Video
Transparent muscle characterization using quantitative electromyography: different binarization mappings.
IEEE Trans Neural Syst Rehabil Eng
PUBLISHED: 04-25-2014
Show Abstract
Hide Abstract
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.
Related JoVE Video
Increased neuromuscular transmission instability and motor unit remodelling with diabetic neuropathy as assessed using novel near fibre motor unit potential parameters.
Clin Neurophysiol
PUBLISHED: 02-24-2014
Show Abstract
Hide Abstract
To assess the degree of neuromuscular transmission variability and motor unit (MU) remodelling in patients with diabetic polyneuropathy (DPN) using decomposition-based quantitative electromyography (DQEMG) and near fibre (NF) motor unit potential (MUP) parameters.
Related JoVE Video
Neurogenic changes in the upper airway of patients with obstructive sleep apnea.
Am. J. Respir. Crit. Care Med.
PUBLISHED: 10-20-2011
Show Abstract
Hide Abstract
Controversy persists regarding the presence and importance of hypoglossal nerve dysfunction in obstructive sleep apnea (OSA).
Related JoVE Video
SVM-based validation of motor unit potential trains extracted by EMG signal decomposition.
IEEE Trans Biomed Eng
PUBLISHED: 09-26-2011
Show Abstract
Hide Abstract
Motor unit potential trains (MUPTs) extracted via electromyographic (EMG) signal decomposition can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid. In this paper, support vector machine (SVM)-based supervised classifiers are proposed to estimate the validity of extracted MUPTs. The classifiers use either the MU firing pattern or the MUP shape consistency of an MUPT, or both, to estimate its validity. The developed classifiers estimate the class label of an MUPT (i.e., valid/invalid) and a degree of support for the decision being made. A single SVM that estimates the validity of a given MUPT using extracted MU firing pattern and MUP shape features was investigated. In addition, the effectiveness of multiclassifier techniques which estimate the overall validity of a train by fusing the MU firing pattern and MUP shape validity of a given MUPT, determined separately by two distinct SVMs, was also investigated. Training based only on simulated data showed robust classification performance of the several multiclassifier methods when tested using both simulated and real test data. Of the methods studied, the multiclassifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance. Assuming 12.7% of extracted MUPTs are on average invalid, the estimated accuracy for this method in correctly categorizing MUPTs extracted during decomposition was 99.4% and 98.8% for simulated and real data, respectively.
Related JoVE Video
A method for detecting and editing MUPTs contaminated by false classification errors during EMG signal decomposition.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 08-29-2011
Show Abstract
Hide Abstract
A robust method for detecting motor unit potential trains (MUPTs) contaminated with false classification errors (FCEs) during EMG signal decomposition and then removing the FCEs from a contaminated train is presented. Using motor unit (MU) firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and motor unit potential (MUP) shape information to detect MUPs that were erroneously assigned to the train (i.e., represent FCEs). For the simulated data used in this study contaminated MUPTs could be detected with 88.7% accuracy. For a given contaminated MUPT, the algorithm on average correctly detected 83.4% of the FCEs and left 93.4% of the correctly assigned MUPs. The accuracy of the MUPs classified to a MUPT was estimated to be 92.1% on average.
Related JoVE Video
Adaptive motor unit potential train validation using MUP shape information.
Med Eng Phys
PUBLISHED: 01-26-2011
Show Abstract
Hide Abstract
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.
Related JoVE Video
A review of clinical quantitative electromyography.
Crit Rev Biomed Eng
PUBLISHED: 12-24-2010
Show Abstract
Hide Abstract
Information regarding the morphology of motor unit potentials (MUPs) and motor unit firing patterns can be used to help diagnose, treat, and manage neuromuscular disorders. In a conventional electromyographic (EMG) examination, a clinician manually assesses the characteristics of needle-detected EMG signals across a number of distinct needle positions and forms an overall impression of the condition of the muscle. Such a subjective assessment is highly dependent on the skills and level of experience of the clinician, and is prone to a high error rate and operator bias. Quantitative methods have been developed to characterize MUP waveforms using statistical and probabilistic techniques that allow for greater objectivity and reproducibility in supporting the diagnostic process. In this review, quantitative EMG (QEMG) techniques ranging from simple reporting of numeric MUP values to interpreted muscle characterizations are presented and reviewed in terms of their clinical potential to improve status quo methods. QEMG techniques are also evaluated in terms of their suitability for use in a clinical decision support system based on previously established criteria. Aspects of prototype clinical decision support systems are then presented to illustrate some of the concepts of QEMG-based decision making.
Related JoVE Video
Intramuscular EMG signal decomposition.
Crit Rev Biomed Eng
PUBLISHED: 12-24-2010
Show Abstract
Hide Abstract
Information regarding motor unit potentials (MUPs) and motor unit fi ring patterns during muscle contractions is useful for physiological investigation and clinical examinations either for the understanding of motor control or for the diagnosis of neuromuscular disorders. In order to obtain such information, composite electromyographic (EMG) signals are decomposed (i.e., resolved into their constituent motor unit potential trains [MUPTs]). The goals of automatic decomposition techniques are to create a MUPT for each motor unit that contributed significant MUPs to the original composite signal. Diagnosis can then be facilitated by decomposing a needle-detected EMG signal, extracting features of MUPTs, and finally analyzing the extracted features (i.e., quantitative electromyography). Herein, the concepts of EMG signals and EMG signal decomposition techniques are explained. The steps involved with the decomposition of an EMG signal and the methods developed for each step, along with their strengths and limitations, are discussed and compared. Finally, methods developed to evaluate decomposition algorithms and assess the validity of the obtained MUPTs are reviewed and evaluated.
Related JoVE Video
Evaluation of motor unit placement algorithms for EMG simulation.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
Show Abstract
Hide Abstract
Motor unit layout algorithms have a significant effect on motor unit fibre densities recorded. Motor unit fibre densities are affected by both the method used to place the motor unit territories, and the mechanism by which muscle fibres are assigned to motor units. The first of these should emulate the process by which separate motor neurons create overlapping territories that cover the muscle cross section, while the second should have some relation to the processes involved with axonal arborization and development of the spatial dispersion of the neuro-muscular junctions. The success of an algorithm in creating physiologically realistic motor unit layouts may be evaluated, in part, by examining the distribution of the muscle fibres assigned to the motor units. This paper examines the motor unit fibre densities found in muscles created by two recent algorithms, and explores the degree to which the concepts used by these algorithms may be shared.
Related JoVE Video
Probabilistic muscle characterization using quantitative electromyography: application to facioscapulohumeral muscular dystrophy.
Muscle Nerve
PUBLISHED: 08-27-2010
Show Abstract
Hide Abstract
Based on quantitative electromyography, a muscle can be categorized as normal or affected by a neuromuscular disorder. The objective of this work was to compare the utility of probabilistic to conventional means and outlier methods of categorization of myopathic and normal muscles. Various sets of motor unit potential (MUP) features detected in biceps brachii muscles of control subjects and patients with facioscapulohumeral muscular dystrophy were used to categorize them as normal or myopathic based on conventional means and outlier categorization (CMC) as well as a new probabilistic muscle categorization (PMC). The sensitivity, specificity, and accuracy provided by each categorization method were compared. The categorizations made using PMC were significantly more accurate (by at least 10%) compared with CMC (P < 10(-10)) for muscles evaluated in this study. Area, duration, and thickness were highly discriminative MUP features.
Related JoVE Video
Assessing motor deficits in compressive neuropathy using quantitative electromyography.
J Neuroeng Rehabil
PUBLISHED: 08-11-2010
Show Abstract
Hide Abstract
Studying 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.
Related JoVE Video
Validating motor unit firing patterns extracted by EMG signal decomposition.
Med Biol Eng Comput
PUBLISHED: 03-03-2010
Show Abstract
Hide Abstract
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.
Related JoVE Video
Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography.
J Neuroeng Rehabil
PUBLISHED: 02-15-2010
Show Abstract
Hide Abstract
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.
Related JoVE Video
MUP shape-based validation of a motor unit potential train.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 12-08-2009
Show Abstract
Hide Abstract
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.
Related JoVE Video
Validation of motor unit potential trains using motor unit firing pattern information.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 12-08-2009
Show Abstract
Hide Abstract
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.
Related JoVE Video
Integrating heterogeneous classifier ensembles for EMG signal decomposition based on classifier agreement.
IEEE Trans Inf Technol Biomed
PUBLISHED: 01-20-2009
Show Abstract
Hide Abstract
In this paper, we present a design methodology for integrating heterogeneous classifier ensembles by employing a diversity-based hybrid classifier fusion approach, whose aggregator module consists of two classifier combiners, to achieve an improved classification performance for motor unit potential classification during electromyographic (EMG) signal decomposition. Following the so-called overproduce and choose strategy to classifier ensemble combination, the developed system allows the construction of a large set of base classifiers, and then automatically chooses subsets of classifiers to form candidate classifier ensembles for each combiner. The system exploits kappa statistic diversity measure to design classifier teams through estimating the level of agreement between base classifier outputs. The pool of base classifiers consists of different kinds of classifiers: the adaptive certainty-based, the adaptive fuzzy k -NN, and the adaptive matched template filter classifiers; and utilizes different types of features. Performance of the developed system was evaluated using real and simulated EMG signals, and was compared with the performance of the constituent base classifiers. Across the EMG signal datasets used, the developed system had better average classification performance overall, especially in terms of reducing classification errors. For simulated signals of varying intensity, the developed system had an average correct classification rate CCr of 93.8% and an error rate Er of 2.2% compared to 93.6% and 3.2%, respectively, for the best base classifier in the ensemble. For simulated signals with varying amounts of shape and/or firing pattern variability, the developed system had a CCr of 89.1% with an Er of 4.7% compared to 86.3% and 5.6%, respectively, for the best classifier. For real signals, the developed system had a CCr of 89.4% with an Er of 3.9% compared to 84.6% and 7.1%, respectively, for the best classifier.
Related JoVE Video
Muscle categorization using PDF estimation and Naive Bayes classification.
Conf Proc IEEE Eng Med Biol Soc
Show Abstract
Hide Abstract
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.
Related JoVE Video
Augmenting the decomposition of EMG signals using supervised feature extraction techniques.
Conf Proc IEEE Eng Med Biol Soc
Show Abstract
Hide Abstract
Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). In this work, the possibility of improving the decomposing results using two supervised feature extraction methods, i.e., Fisher discriminant analysis (FDA) and supervised principal component analysis (SPCA), is explored. Using the MUP labels provided by a decomposition-based quantitative EMG system as a training data for FDA and SPCA, the MUPs are transformed into a new feature space such that the MUPs of a single MU become as close as possible to each other while those created by different MUs become as far as possible. The MUPs are then reclassified using a certainty-based classification algorithm. Evaluation results using 10 simulated EMG signals comprised of 3-11 MUPTs demonstrate that FDA and SPCA on average improve the decomposition accuracy by 6%. The improvement for the most difficult-to-decompose signal is about 12%, which shows the proposed approach is most beneficial in the decomposition of more complex signals.
Related JoVE Video
EMG signal decomposition using motor unit potential train validity.
IEEE Trans Neural Syst Rehabil Eng
Show Abstract
Hide Abstract
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.
Related JoVE Video
Feature selection for motor unit potential train characterization.
Muscle Nerve
Show Abstract
Hide Abstract
Introduction: Ten new features of motor unit potential (MUP) morphology and stability are proposed. These new features, along with 8 traditional features, are grouped into 5 aspects: size, shape, global complexity, local complexity, and stability. Methods: We used sequential forward and backward search strategies to select subsets of these 18 features that can be used to discriminate accurately between muscles whose MUPs are predominantly neurogenic, myopathic, or normal. Discussion: Results based on 8102 motor unit potential trains (MUPTs) extracted from 4 different limb muscles (n=336 total muscles) demonstrate the usefulness of these newly introduced features and support an aspect-based grouping of MUPT features. © 2013 Wiley Periodicals, Inc.
Related JoVE Video

What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.