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Daniel W Stashuk - One of the best experts on this subject based on the ideXlab platform.

  • a method for editing Motor Unit Potential trains obtained by decomposition of surface electromyographic signals
    Journal of Electromyography and Kinesiology, 2020
    Co-Authors: Robert I Kumar, Daniel W Stashuk, Matt M Mallette, Stephen S Cheung, David A Gabriel
    Abstract:

    Abstract Rather than discarding Motor Unit Potential trains (MUPTs) because they do not meet 100% validity criteria, we describe and evaluate a novel editing routine that preserves valid discharge times, based on decreasing shape variability (variance ratio, VR) within a MUPT. The error filtered estimation (EFE) algorithm is then applied to the remaining ‘high confidence’ discharge times to estimate inter-discharge interval (IDI) statistics. Decomposed surface EMG data from the flexor carpi radialis recorded from 20 participants during 60% MVC wrist flexion was used. There were two levels of denoising criteria (relaxed and strict) criteria for removing MUPs to decrease the VR and increase the signal-to-noise ratio (SNR) of a MUPT. In total, VR decreased 24.88% and SNR increased 6.0% (p’s   0.05). The same was true for the SEE between denoising criteria, which increased only to 5.14% for the strict criteria (p > 0.05). Editing the MUPTs resulted in a significant decrease in MUP shape variability and in the measures extracted from the MUP templates, with trivial differences between the SEE of the mean IDI between the edited and unedited MUPTs.

  • Discovering Density-Based Clustering Structures Using Neighborhood Distance Entropy Consistency
    IEEE Transactions on Computational Social Systems, 2020
    Co-Authors: Tahereh Kamali, Daniel W Stashuk
    Abstract:

    Traditional clustering algorithms model the clustering problem as an optimization task, in which the objective is defined based on minimizing specific metrics. These algorithms are limited to find clusters with convex polytopes. In contrast, density-based clustering algorithms aim at overcoming this limitation and try to partition data objects into meaningful groups that have relatively high density separated by low-density regions. This work describes and evaluates a new density-based clustering algorithm, called neighborhood distance entropy consistency (NDEC), which is able to not only detect clusters of arbitrary size, shape, and density, but also identify outliers. To this end, both local and global densities are considered simultaneously to accurately discover the intrinsic clustering structure. In addition, the consistency of neighborhood distance entropy is used as an important criterion to merge Potential subclusters. Experiments on synthetic and real benchmark clustering data sets have demonstrated the efficiency and effectiveness of the NDEC method. Comparisons with ${k}$ -means, DBSCAN, OPTICS, and density peaks clustering algorithms further show that NDEC can successfully discover natural clusters. Additionally, the utility of NDEC is demonstrated with its application on two real-world problems including brain white matter tracts segmentation using diffusion tensor imaging and characterizing Motor Unit Potential trains extracted from electromyographic signals.

  • P36-S Validation of near fiber Motor Unit Potential stability and dispersion measures using simulated signals
    Clinical Neurophysiology, 2019
    Co-Authors: Oscar Garnes C. Estruch, Daniel W Stashuk
    Abstract:

    Background A Motor Unit Potential (MUP) is the summation of the muscle fiber Potentials (MFPs) generated by the fibers of its MU. A near-fiber (NF) MUP is the summation of MFPs from fibers near the recording electrode. The temporal consistency and dispersion of NF MFP contributions can be clinically useful. NF MUP jiggle and NF MUP segment jitter are new measures of MFP temporal consistency and NF duration and NF dispersion are new measures of MFP temporal dispersion. Simulated EMG models provide a controlled scenario for validating the overarching capabilities of these new NF MUP measures. Material and methods A commercial EMG simulator (Karlsson and Stalberg, version 3.6) generated 120 EMG signals each comprised of 4 Motor Unit Potential trains (MUPTs). Across these signals, MU size varied between 100 to 300 fibers, MFP jitter ranged from 20 to 80  μ s and three different recording electrode to endplate distances (5, 20 and 55 mm) were used. MUPTs were extracted from the EMG signals and analyzed using DQEMG. Results NF MUP jiggle and NF MUP segment jitter show high accuracy in evaluating NF MFP jitter. NF-MUP duration and dispersion offer accurate information about MU morphology (i.e. dispersion of fiber end plate locations and varying conduction velocities). Conclusions The new NF MUP measures accurately represent useful electrophysiological and morphological aspects of their corresponding MUs. DQEMG offers a comprehensive evaluation of Motor Unit electrophysiological stability and morphology that can assist with the diagnosis of neuromuscular diseases.

  • Improved MUP Template Estimation Using Local Time Warping and Kernel Weighted Averaging
    2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
    Co-Authors: Andrew Hamilton-wright, Daniel W Stashuk
    Abstract:

    A Motor Unit Potential (MUP) template, which represents the shapes of the MUPs within a MUP train, provides information related to the morphology and physiology of the sampled Motor Unit. This work presents an improved MUP template estimation technique that uses local time warping and kernel weighted ensemble averaging. An analysis of the algorithm, and a description of the improvements compared with spike triggered averaging is given. MUP template estimates were evaluated using simulated EMG signals with a known gold standard template for each Motor Unit Potential train. Statistically significant reduction in template estimation error is shown, both within the baseline and duration portions of a MUP.

  • Cross Comparison of Motor Unit Potential Features Used in EMG Signal Decomposition
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018
    Co-Authors: Mohsen Ghofrani Jahromi, Hossein Parsaei, Ali Zamani, Daniel W Stashuk
    Abstract:

    Feature extraction is an important step of resolving an electromyographic (EMG) signal into its component Motor Unit Potential trains, commonly known as EMG decomposition. Until now, different features have been used to represent Motor Unit Potentials (MUPs) and improve decomposition processing time and accuracy, but a major limitation is that no systematic comparison of these features exists. In an EMG decomposition system, like any pattern recognition system, the features used for representing MUPs play an important role in the overall performance of the system. A cross comparison of the feature extraction methods used in EMG signal decomposition can assist in choosing the best features for representing MUPs and ultimately may improve EMG decomposition results. This paper presents a survey and cross comparison of these feature extraction methods. Decomposability index, classification accuracy of a k -nearest neighbors classifier, and class-feature mutual information were employed for evaluating the discriminative power of various feature extraction techniques commonly used in the literature including time domain, morphological, frequency domain, and discrete wavelets. In terms of data, 45 simulated and 82 real EMG signals were used. Results showed that among time domain features, the first derivative of time samples exhibit the best separability. For morphological features, slope analysis provided the most discriminative power. Discrete Fourier transform coefficients offered the best separability among frequency domain features. However, neither morphological nor frequency domain techniques outperformed time domain features. The detail 4 coefficients in a discrete wavelets decomposition exceeded in evaluation measures when compared with other feature extraction techniques. Using principal component analysis slightly improved the results, but it is time consuming. Overall, considering computation time and discriminative ability, the first derivative of time samples might be efficient in representing MUPs in EMG decomposition and there is no need for sophisticated feature extraction methods.

Simon Podnar - One of the best experts on this subject based on the ideXlab platform.

  • predictive values of Motor Unit Potential analysis in limb muscles
    Clinical Neurophysiology, 2009
    Co-Authors: Simon Podnar
    Abstract:

    Abstract Objective In interpretation of diagnostic findings the probability that an abnormal test accurately indicates pathology (i.e., the positive predictive value), and a normal test accurately excludes pathology (i.e., the negative predictive value) is the most important. For Motor Unit Potential (MUP) analysis no such data has been published; hence this was the aim of this study. Methods In 31 patients with facioscapulohumeral muscular dystrophy (FSHD) and 34 controls the biceps brachii and vastus lateralis muscles were examined by concentric needle electromyography (EMG), using template operated MUP analysis. These results were compared to non-parametric reference data obtained in another group of 34 (biceps brachii) and 46 (vastus lateralis) control subjects. Results For the biceps brachii muscles sensitivity was 59%, specificity 91%, the positive predictive value 85%, and negative predictive value 72% with at least two criteria (mean values or outliers for MUP thickness, amplitude and duration) below the reference intervals. In addition, all subjects with three abnormal EMG criteria were FSHD patients, and 90% of subjects with normal EMG were controls. Conclusions Template operated MUP analysis demonstrated reasonable predictive value for diagnosis and exclusion of myopathy. Significance Quantitative MUP analysis seems to be useful for the preliminary diagnosis of FSHD in patients with appropriate clinical picture.

  • comparison of parametric and nonparametric reference data in Motor Unit Potential analysis
    Muscle & Nerve, 2008
    Co-Authors: Simon Podnar
    Abstract:

    For calculation of outlier reference intervals, by definition nonparametric statistics are applied, while for mean value reference intervals parametric or nonparametric statistics can be used. The aim of this study was to compare the mean value reference intervals and their sensitivity for diagnosis of myopathy. Quantitative concentric needle electromyography (EMG) of the biceps brachii muscle was performed using multi–Motor Unit Potential (MUP) analysis. In 34 healthy subjects both parametric (mean ± 2SD) and nonparametric (2.5th–97.5th percentiles) reference intervals were calculated for mean values of MUP parameters, while for outliers nonparametric reference intervals (5th–95th percentiles) were calculated. Their sensitivity was tested in 29 patients with facioscapulohumeral muscular dystrophy. Nonparametric reference intervals were narrower than parametric intervals, which resulted in slightly higher sensitivities when combined with outlier limits (e.g., thickness = 86% and 83%, respectively). Muscle Nerve, 2008

  • quantitative Motor Unit Potential analysis in the diaphragm a normative study
    Muscle & Nerve, 2008
    Co-Authors: Simon Podnar, Anita Resmangaspersic
    Abstract:

    Although quantitative Motor Unit Potential (MUP) analysis has an established role in other skeletal muscles, it has not been performed in the diaphragm. The aim of the present study was to test whether such studies are possible and to establish normative data. Twenty-nine healthy volunteers (15 men), aged 21-65 years (median, 33 years), were studied using standard concentric needle electrodes, and equipment with the facility for template-operated multi-MUP analysis. Needle electrodes were inserted into the right medial recess of the seventh to ninth intercostal spaces. During MUP sampling, subjects were asked to hold their breath in partial inspiration for 5 s. At least 20 MUPs were obtained in 28 subjects. Diaphragmatic MUPs were confirmed to be much smaller than those of limb muscles. We found diaphragmatic quantitative MUP analysis to be possible in healthy volunteers. However, further studies in patient groups are needed to establish the feasibility and clinical value of such studies.

  • sensitivity of Motor Unit Potential analysis in facioscapulohumeral muscular dystrophy
    Muscle & Nerve, 2006
    Co-Authors: Simon Podnar, Janez Zidar
    Abstract:

    Template-operated Motor Unit Potential (MUP) analysis has made quantitative electromyography (EMG) feasible, even in busy laboratories, but validation of this approach is still necessary. In the present study, the utility of multi-MUP analysis was assessed in patients with a molecular genetic diagnosis of facioscapulohumeral muscular dystrophy (FSHD). Manual assessment of muscle strength and concentric-needle EMG of the biceps brachii and vastus lateralis muscles were performed. The sensitivity for diagnosing myopathy (mean values and outliers) was tested for eight MUP parameters and four of their combinations. The group comprised 31 patients. Elbow flexion and knee extension strength was normal in 45% and 52% of patients, respectively. The most sensitive MUP parameter was thickness, followed by duration. A combination of three MUP parameters (thickness, amplitude, and duration/area) was needed for maximal sensitivity. The study demonstrated a high sensitivity of multi-MUP analysis in FSHD. Myopathic abnormalities were demonstrated in all weak biceps brachii muscles, and in 77% of biceps brachii muscles with normal strength. Muscle Nerve, 2006

  • Evaluation of the complexity of Motor Unit Potentials in anal sphincter electromyography.
    Clinical Neurophysiology, 2004
    Co-Authors: Simon Podnar, Ewa Zalewska, Irena Hausmanowa-petrusewicz
    Abstract:

    Abstract Objective Motor Unit Potential (MUP) morphology can be quantified using parameters describing electrophysiological size and shape (complexity). Traditionally, MUP complexity has been estimated using parameters number of phases and turns. Recently, ‘irregularity coefficient’ (IR), measuring the length of the MUP curve normalized with its amplitude, has been introduced. The aim of this study was to evaluate IR in the external anal sphincter muscle. Methods Sensitivity was examined in 61 patients with chronic cauda equina lesions, and specificity in 75 controls using a standard concentric EMG needle and EMG system with multi-MUP analysis. Results When evaluated separately the sensitivity of IR was 16% lower, and specificity 5% higher compared to number of turns, with both differences decreasing to only 2 and 1%, respectively, when each of these parameters was added to MUP area and duration. Conclusions Our present results suggest that IR provides a similar diagnostic usefulness, but a more appropriate description of MUP complexity compared to traditional MUP parameters. Significance IR seems to be suitable to complement one of MUP parameters measuring electrophysiological MUP size in the future quantitative EMG.

Mohamed S. Kamel - One of the best experts on this subject based on the ideXlab platform.

  • CCECE - Classifier fusion interactive software toolbox for EMG signal decomposition
    2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), 2015
    Co-Authors: Sarbast Rasheed, Daniel W Stashuk, Mohamed S. Kamel
    Abstract:

    A classifier fusion interactive software package has been constructed for implementing the classification task in the electromyographic (EMG) signal decomposition process using the MATLAB high-level programming language and its interactive environment. The package employs classifier fusion schemes of multiple classifier combination for the purpose of fusing the decisions of a set of heterogeneous base classifiers to make a final decision that achieves improved classification performance. The base classifiers used are ensembles of error-independent certainty, fuzzy k-NN, and template matched filter classifiers. The interactive package consists of several graphical user interfaces (GUIs) to extract individual Motor Unit Potential (MUP) waveforms from raw EMG signals; extract relevant features; classify MUPs into Motor Unit Potential trains (MUPTs) using certainty-based, assertion-based, and similarity-based classifiers; and combine classifier decisions. The proposed software package is useful for enhancing the EMG signal analysis quality and providing a systematic approach to the EMG signal decomposition process. It worked as a very helpful environment for testing and evaluating algorithms developed for EMG signal decomposition research.

  • Classifier fusion interactive software toolbox for EMG signal decomposition
    Canadian Conference on Electrical and Computer Engineering, 2015
    Co-Authors: Shahnawaz Rasheed, Daniel W Stashuk, Mohamed S. Kamel
    Abstract:

    © 2015 IEEE.A classifier fusion interactive software package has been constructed for implementing the classification task in the electromyographic (EMG) signal decomposition process using the MATLAB high-level programming language and its interactive environment. The package employs classifier fusion schemes of multiple classifier combination for the purpose of fusing the decisions of a set of heterogeneous base classifiers to make a final decision that achieves improved classification performance. The base classifiers used are ensembles of error-independent certainty, fuzzy k-NN, and template matched filter classifiers. The interactive package consists of several graphical user interfaces (GUIs) to extract individual Motor Unit Potential (MUP) waveforms from raw EMG signals; extract relevant features; classify MUPs into Motor Unit Potential trains (MUPTs) using certainty-based, assertion-based, and similarity-based classifiers; and combine classifier decisions. The proposed software package is useful for enhancing the EMG signal analysis quality and providing a systematic approach to the EMG signal decomposition process. It worked as a very helpful environment for testing and evaluating algorithms developed for EMG signal decomposition research.

  • Augmenting the decomposition of EMG signals using supervised feature extraction techniques
    2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Hossein Parsaei, Daniel W Stashuk, Mehrdad J. Gangeh, Mohamed S. Kamel
    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.

  • An interactive environment for Motor Unit Potential classification using certainty-based classifiers
    Simulation Modelling Practice and Theory, 2008
    Co-Authors: Sarbast Rasheed, Daniel W Stashuk, Mohamed S. Kamel
    Abstract:

    Abstract An interactive environment for performing the Motor Unit Potential (MUP) classification tasks required for electromyographic (EMG) signal decomposition is described and developed utilizing the MATLAB high-level programming language and its interactive environment. Certainty-based classification approach had been employed for the classification task in which the assignment criterion used for MUPs is based on a combination of MUP shapes and Motor Unit firing pattern information. The environment software package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into Motor Unit Potential trains (MUPTs) using certainty-based classifiers. The development of the proposed software package is useful at enhancing the analysis quality and providing a systematic approach to the EMG signal decomposition process.

  • fusion of multiple classifiers for Motor Unit Potential sorting
    Biomedical Signal Processing and Control, 2008
    Co-Authors: Sarbast Rasheed, Daniel W Stashuk, Mohamed S. Kamel
    Abstract:

    Abstract To achieve improved classification performance, a multiclassifier fusion approach for Motor Unit Potential (MUP) sorting during electromyographic (EMG) signal decomposition was investigated. A classifier fusion system was developed that aggregates, at the abstract and measurement levels, the outputs of an ensemble of heterogeneous base classifiers to reach a collective decision, and then uses an adaptive feedback control system that detects and processes classification errors by using Motor Unit firing pattern consistency statistics. Three types of base classifiers were used: certainty, adaptive certainty, and adaptive fuzzy k-NN. Performance of the developed system was evaluated using real and synthetic simulated EMG signals with known properties and compared with the performance of the constituent base classifiers. Across the sets of EMG signal data sets studied, the classifier fusion schemes had better average classification performance, especially in terms of improving correct classification rates. Relative to the average performance of base classifiers and based on the difference between correct classification rate CC r and error rate E r , the adaptive average rule classifier fusion scheme shows on average: for the set of real signals an improvement of 9.2%; for the set of simulated signals of varying intensity an improvement of 6%; and for the set of simulated signals of varying amounts of shape and/or firing pattern variability an improvement of 7.7%.

Hossein Parsaei - One of the best experts on this subject based on the ideXlab platform.

  • Can Wavelet Denoising Improve Motor Unit Potential Template Estimation
    Journal of biomedical physics & engineering, 2020
    Co-Authors: S H Hasanzadeh, Hossein Parsaei, Mohammad Mehdi Movahedi
    Abstract:

    Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in Motor Unit Potentials (MUPs) of its Motor Units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial. Objective: To investigate the possibility of improving MUP template estimation using the wavelet denoising technique. Material and Methods: In this analytical study, several MUP template estimators were developed by combining conventional estimation methods and wavelet denoising techniques. A MUP template was initially estimated using conventional methods such as mean, median, median-trimmed mean, or mode. Thereafter, it was post-processed using the wavelet denoising technique. Results: Evaluation results of the studied estimators using 40 simulated EMG signals with a true template for each constituent MUP train showed that augmented wavelet- based template estimation methods are more reliable than conventional methods. However, on average, wavelet denoising was not much effective. Around 40 MUPs of a MU is sufficient to estimate its MUP template. Conclusions: Although wavelet techniques are effective in EMG signal analysis, here wavelet denoising did not practically improve MUP template estimation. Considering computational simplicity and estimation error, the two methods median and median-trimmed mean are practical estimators that can provide a good estimation of a MUP template for a MU when approximately 40 MUPs are available. Nevertheless, the baseline noise level in the MUP templates estimated using the median-trimmed mean method is slightly lower than that in the templates estimated using the median method.

  • Cross Comparison of Motor Unit Potential Features Used in EMG Signal Decomposition
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018
    Co-Authors: Mohsen Ghofrani Jahromi, Hossein Parsaei, Ali Zamani, Daniel W Stashuk
    Abstract:

    Feature extraction is an important step of resolving an electromyographic (EMG) signal into its component Motor Unit Potential trains, commonly known as EMG decomposition. Until now, different features have been used to represent Motor Unit Potentials (MUPs) and improve decomposition processing time and accuracy, but a major limitation is that no systematic comparison of these features exists. In an EMG decomposition system, like any pattern recognition system, the features used for representing MUPs play an important role in the overall performance of the system. A cross comparison of the feature extraction methods used in EMG signal decomposition can assist in choosing the best features for representing MUPs and ultimately may improve EMG decomposition results. This paper presents a survey and cross comparison of these feature extraction methods. Decomposability index, classification accuracy of a k -nearest neighbors classifier, and class-feature mutual information were employed for evaluating the discriminative power of various feature extraction techniques commonly used in the literature including time domain, morphological, frequency domain, and discrete wavelets. In terms of data, 45 simulated and 82 real EMG signals were used. Results showed that among time domain features, the first derivative of time samples exhibit the best separability. For morphological features, slope analysis provided the most discriminative power. Discrete Fourier transform coefficients offered the best separability among frequency domain features. However, neither morphological nor frequency domain techniques outperformed time domain features. The detail 4 coefficients in a discrete wavelets decomposition exceeded in evaluation measures when compared with other feature extraction techniques. Using principal component analysis slightly improved the results, but it is time consuming. Overall, considering computation time and discriminative ability, the first derivative of time samples might be efficient in representing MUPs in EMG decomposition and there is no need for sophisticated feature extraction methods.

  • EMG Signal Decomposition Using Motor Unit Potential Train Validity
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013
    Co-Authors: Hossein Parsaei, Daniel W Stashuk
    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 (Ae), assignment rate (Ar), correct classification rate (CCr), and the error in estimating the number of MUPTs represented in the set of detected MUPs (ENMUPTs) was evaluated using 32 simulated and 30 real EMG signals comprised of 3-11 and 3-15 MUPTs, respectively. The developed system, with average CCr of 86.4% for simulated and 96.4% for real data, outperformed a previously developed EMG decomposition system, with average CCr of 71.6% and 89.7% for simulated and real data, by 14.7% and 6.7%, respectively. In terms of ENMUPTs, the new system, with average ENMUPTs 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 ENMUPTs of 2.2 and 0.8 for simulated and real data respectively. For both the simulated and real data used, variations in Ac, Ar, and ENMUPTs 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.

  • Motor Unit Potential Train Validation and Its Application in EMG Signal Decomposition
    Applied Biological Engineering - Principles and Practice, 2012
    Co-Authors: Hossein Parsaei, Daniel W Stashuk
    Abstract:

    Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent Motor Unit Potential trains (MUPTs). The purpose of EMG signal decomposition is to provide an estimate of the firing pattern and Motor Unit Potential (MUP) template of each active Motor Unit (MU) that contributed significant MUPs to the EMG signal. The extracted MU firing patterns, MUP templates, and their estimated feature values can assist with the diagnosis of neuromuscular disorders (Stalberg & Falck, 1997; Troger & Dengler, 2000; Fuglsang-Frederiksen, 2006; Pino et al., 2008; Farkas et al., 2010), the understanding of Motor control ( De Luca et al. 1982a, 1982b; Contessa et al.,2009), and the characterization of MU architecture (Lateva & McGill, 2001), but only if they are valid trains. Depending on the complexity of the signal being decomposed, the variability of MUP shapes and MU firing patterns, and the criteria and parameters used by the decomposition algorithm to merge or split the obtained MUPTs, several invalid MUPTs may be created.

  • SVM-Based Validation of Motor Unit Potential Trains Extracted by EMG Signal Decomposition
    IEEE Transactions on Biomedical Engineering, 2012
    Co-Authors: Hossein Parsaei, Daniel W Stashuk
    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.

Timothy J Doherty - One of the best experts on this subject based on the ideXlab platform.

  • increased Motor Unit Potential shape variability across consecutive Motor Unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects
    Clinical Neurophysiology, 2015
    Co-Authors: Maddison L Hourigan, Daniel W Stashuk, Neal B Mckinnon, Marjorie Johnson, Charles L Rice, Timothy J Doherty
    Abstract:

    Abstract Objective To study the Potential utility of using near fiber (NF) jiggle as an assessment of neuromuscular transmission stability in healthy older subjects using decomposition-based quantitative electromyography (DQEMG). Methods The tibialis anterior (TA) and vastus medialis (VM) muscles were tested in 9 older men (77 ± 5 years) and 9 young male control subjects (23 ± 0.3 years). Simultaneous surface and needle-detected electromyographic (EMG) signals were collected during voluntary contractions, and then analyzed using DQEMG. Motor Unit Potential (MUP) and NF MUP parameters were analyzed. Results NF jiggle was significantly increased for both the TA and VM in the old age group relative to the younger controls ( P P P P Conclusions Healthy aging is associated with neuromuscular transmission instability (increased NF jiggle) and MU remodeling, which can be measured using DQEMG. Significance NF jiggle derived from DQEMG can be a useful method of identifying neuromuscular dysfunction at various stages of MU remodeling and aging.

  • increased neuromuscular transmission instability and Motor Unit remodelling with diabetic neuropathy as assessed using novel near fibre Motor Unit Potential parameters
    Clinical Neurophysiology, 2015
    Co-Authors: Matti D Allen, Daniel W Stashuk, Maddison L Hourigan, Timothy J Doherty, Kurt Kimpinski, Charles L Rice
    Abstract:

    Abstract Objective 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. Methods The tibialis anterior (TA) muscle was tested in 12 patients with DPN (65±15years) and 12 controls (63±15years). DQEMG was used to analyze electromyographic (EMG) signals collected during voluntary contractions. MUP and NF MUP parameters were analyzed. NF MUPs were obtained by high-pass filtering MUP template waveforms, which isolates contributions of fibres that are close to the needle detection surface. NF MUP parameters provided assessment of Motor Unit size (NF area), fibre density (NF fibre count) and contribution dispersion (NF dispersion) and neuromuscular transmission instability (NF jiggle). Results DPN patients had larger (+45% NF area), more complex (+30% NF fibre count), and less stable (+30% NF jiggle) NF MUPs ( p r =0.63; p r =0.46; p Conclusions DPN is associated with neuromuscular transmission instability and MU remodelling that can be assessed using DQEMG. Significance DQEMG-derived NF MUP parameters may be useful in identifying patients in early stages of neuromuscular dysfunction related to DPN.

  • influence of needle electrode depth on de sta Motor Unit number estimation
    Muscle & Nerve, 2014
    Co-Authors: Colleen T Ives, Timothy J Doherty
    Abstract:

    Introduction: To assess a Potential source of technique-associated error, we evaluated the influence of needle electrode depth on decomposition-enhanced spike-triggered averaging (DE-STA) Motor Unit number estimation (MUNE) and quantitative Motor Unit analysis in the upper trapezius (UT). Methods: The DE-STA MUNE protocol was performed at superficial, intermediate, and deep needle electrode depths in 18 control subjects. Results: Mean surface-detected Motor Unit Potential amplitudes were significantly smaller for intermediate versus superficial (P < 0.05), deep versus superficial (P < 0.001), and deep versus intermediate (P < 0.05). MUNE was significantly larger for deep versus superficial (P < 0.001), with statistical trends toward larger MUNE values at greater depths for the remaining comparisons. No significant differences were found among needle electrode depths for quantitative Motor Unit Potential parameters. Conclusions: These results demonstrate the important influence of needle electrode depth on DE-STA MUNE in the UT. Suggestions are made for improved standardization of the protocol. Muscle Nerve 50: 587–592, 2014

  • Motor Unit Potential characterization using pattern discovery
    Medical Engineering & Physics, 2008
    Co-Authors: L J Pino, Daniel W Stashuk, Timothy J Doherty
    Abstract:

    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 Naive 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.