Relevant bibliographies by topics / Motor Unit Action Potentials (MUAP)

Academic literature on the topic 'Motor Unit Action Potentials (MUAP)'

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Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Motor Unit Action Potentials (MUAP)"

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Sandercock, T. G., J. A. Faulkner, J. W. Albers, and P. H. Abbrecht. "Single motor unit and fiber action potentials during fatigue." Journal of Applied Physiology 58, no. 4 (April 1, 1985): 1073–79. http://dx.doi.org/10.1152/jappl.1985.58.4.1073.

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Muscle fatigue is defined as a loss of tension development during constant stimulation. Although the relationship is not well documented, muscle fatigue has been inferred from electromyogram (EMG) signals. The purpose of this study was to determine the relationship between the amplitude and duration of single motor unit action potentials (MUAPs) and the loss of tension development (fatigue) in the medial gastrocnemius muscles of cats. Single motor units were fatigued by continuous stimulation at 10 or 80 Hz or with trains of 40-Hz stimuli. When motor units were stimulated at 10 Hz and with trains at 40 Hz (low frequency), tension declined and remained depressed during recovery. The changes in the MUAP correlated poorly with changes in tension. During and after stimulation at 80 Hz (high frequency), changes in the amplitude and duration of MUAPs correlated highly with changes in tension development. Since the EMG signal is dependent on a summation and cancellation of individual MUAPs, the EMG provides a reasonable estimate of high-frequency fatigue but an unreliable measure of low-frequency fatigue.
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McManus, Lara, Xiaogang Hu, William Z. Rymer, Madeleine M. Lowery, and Nina L. Suresh. "Changes in motor unit behavior following isometric fatigue of the first dorsal interosseous muscle." Journal of Neurophysiology 113, no. 9 (May 2015): 3186–96. http://dx.doi.org/10.1152/jn.00146.2015.

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The neuromuscular strategies employed to compensate for fatigue-induced muscle force deficits are not clearly understood. This study utilizes surface electromyography (sEMG) together with recordings of a population of individual motor unit action potentials (MUAPs) to investigate potential compensatory alterations in motor unit (MU) behavior immediately following a sustained fatiguing contraction and after a recovery period. EMG activity was recorded during abduction of the first dorsal interosseous in 12 subjects at 20% maximum voluntary contraction (MVC), before and directly after a 30% MVC fatiguing contraction to task failure, with additional 20% MVC contractions following a 10-min rest. The amplitude, duration and mean firing rate (MFR) of MUAPs extracted with a sEMG decomposition system were analyzed, together with sEMG root-mean-square (RMS) amplitude and median frequency (MPF). MUAP duration and amplitude increased immediately postfatigue and were correlated with changes to sEMG MPF and RMS, respectively. After 10 min, MUAP duration and sEMG MPF recovered to prefatigue values but MUAP amplitude and sEMG RMS remained elevated. MU MFR and recruitment thresholds decreased postfatigue and recovered following rest. The increase in MUAP and sEMG amplitude likely reflects recruitment of larger MUs, while recruitment compression is an additional compensatory strategy directly postfatigue. Recovery of MU MFR in parallel with MUAP duration suggests a possible role for metabolically sensitive afferents in MFR depression postfatigue. This study provides insight into fatigue-induced neuromuscular changes by examining the properties of a large population of concurrently recorded single MUs and outlines possible compensatory strategies involving alterations in MU recruitment and MFR.
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Kidd, GL, and JA Oldham. "Motor unit action potential (MUAP) sequence and electrotherapy." Clinical Rehabilitation 2, no. 1 (February 1988): 23–33. http://dx.doi.org/10.1177/026921558800200105.

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Bischoff, Christian, Erik Stålberg, Björn Falck, and Karin Edebol Eeg-Olofsson. "Reference values of motor unit action potentials obtained with multi-MUAP analysis." Muscle & Nerve 17, no. 8 (August 1994): 842–51. http://dx.doi.org/10.1002/mus.880170803.

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Doherty, T. J., A. A. Vandervoort, A. W. Taylor, and W. F. Brown. "Effects of motor unit losses on strength in older men and women." Journal of Applied Physiology 74, no. 2 (February 1, 1993): 868–74. http://dx.doi.org/10.1152/jappl.1993.74.2.868.

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The influence of age-associated motor unit loss on contractile strength was investigated in a representative sample of healthy, active young and older men and women. In 24 younger subjects (22–38 yr) and 20 older subjects (60–81 yr) spike-triggered averaging was employed to extract a sample of surface-recorded single motor unit action potentials (S-MUAPs) from the biceps brachii and brachialis muscles. The amplitude of the maximum compound muscle action potential of the biceps brachii and brachialis muscles was divided by the mean S-MUAP amplitude to estimate the numbers of motor units present. The maximum isometric twitch contraction (MTC) and maximum voluntary contraction (MVC) of the elbow flexors were also recorded in 18 of the younger subjects and in all older subjects. The estimated numbers of motor units were significantly reduced (47%, P < 0.001) in older subjects with a mean value of 189 +/- 77 compared with a mean of 357 +/- 97 in younger subjects. The sizes of the S-MUAPs, however, were significantly larger in older subjects (23%, P < 0.01). Significant but less marked age-associated reductions in the MTC (33%, P < 0.05) and MVC (33%, P < 0.001) were also found and were similar for both men and women. These results suggest that motor unit losses, even in healthy active individuals, are a primary factor in the age-associated reductions in contractile strength.
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Fatela, Pedro, Goncalo V. Mendonca, António Prieto Veloso, Janne Avela, and Pedro Mil-Homens. "Blood Flow Restriction Alters Motor Unit Behavior During Resistance Exercise." International Journal of Sports Medicine 40, no. 09 (July 10, 2019): 555–62. http://dx.doi.org/10.1055/a-0888-8816.

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AbstractWe aimed to determine whether blood flow restriction (BFR) alters the characteristics of individual motor units during low-intensity (LI) exercise. Eight men (26.0±3.8 yrs) performed 5 sets of 15 knee extensions at 20% of one-repetition maximum (with and without BFR). Maximal isometric voluntary contractions (MVC) were performed before and after exercise to quantify force decrement. Submaximal isometric voluntary contractions were additionally performed for 18 s, matching trapezoidal target-force trajectories at 40% pre-MVC. EMG activity was recorded from the vastus lateralis muscle. Then, signals were decomposed to extract motor unit recruitment threshold, firing rates and action potential amplitudes (MUAP). Force decrement was only seen after LI BFR exercise (–20.5%; p<0.05). LI BFR exercise also induced greater decrements in the linear slope coefficient of the regression lines between motor unit recruitment threshold and firing rate (BFR: –165.1±120.4 vs. non-BFR: –44.4±33.1%, p<0.05). Finally, there was a notable shift towards higher values of firing rate and MUAP amplitude post-LI BFR exercise. Taken together, our data indicate that LI BFR exercise increases the activity of motor units with higher MUAP amplitude. They also indicate that motor units with similar MUAP amplitudes become activated at higher firing rates post-LI BFR exercise.
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De Luca, Carlo J., Shey-Sheen Chang, Serge H. Roy, Joshua C. Kline, and S. Hamid Nawab. "Decomposition of surface EMG signals from cyclic dynamic contractions." Journal of Neurophysiology 113, no. 6 (March 15, 2015): 1941–51. http://dx.doi.org/10.1152/jn.00555.2014.

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Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.
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Boonstra, Tjeerd W., and Michael Breakspear. "Neural mechanisms of intermuscular coherence: implications for the rectification of surface electromyography." Journal of Neurophysiology 107, no. 3 (February 2012): 796–807. http://dx.doi.org/10.1152/jn.00066.2011.

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Oscillatory activity plays a crucial role in corticospinal control of muscle synergies and is widely investigated using corticospinal and intermuscular synchronization. However, the neurophysiological mechanisms that translate these rhythmic patterns into surface electromyography (EMG) are not well understood. This is underscored by the ongoing debate on the rectification of surface EMG before spectral analysis. Whereas empirical studies commonly rectify surface EMG, computational approaches have argued against it. In the present study, we employ a computational model to investigate the role of the motor unit action potential (MAUP) on the translation of oscillatory activity. That is, diverse MUAP shapes may distort the transfer of common input into surface EMG. We test this in a computational model consisting of two motor unit pools receiving common input and compare it to empirical results of intermuscular coherence between bilateral leg muscles. The shape of the MUAP was parametrically varied, and power and coherence spectra were investigated with and without rectification. The model shows that the effect of EMG rectification depends on the uniformity of MUAP shapes. When output spikes of different motor units are convolved with identical MUAPs, oscillatory input is evident in both rectified and nonrectified EMG. In contrast, a heterogeneous MAUP distribution distorts common input and oscillatory components are only manifest as periodic amplitude modulations, i.e., in rectified EMG. The experimental data showed that intermuscular coherence was mainly discernable in rectified EMG, hence providing empirical support for a heterogeneous distribution of MUAPs. These findings implicate that the shape of MUAPs is an essential parameter to reconcile experimental and computational approaches.
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Bossaghzadeh, Zeynab, Firoozeh Niazvand, Medi Saneie, Shahram Rahimi-Dehgolan, Hooshan Sahariati Ghadikolaei, and Sara Mobarak. "Common Peroneal Nerve Injury in a Patient with COVID-19 Infection." Bionatura 3, no. 3 (August 15, 2021): 2043–45. http://dx.doi.org/10.21931/rb/2021.06.03.26.

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This report described a 46-year man with the characteristic Computerized Tomography (CT) scan findings of Corona Virus Disease Infection 19 (COVID-19) who presented to the hospital with right ankle weakness three weeks after the pneumonitis. He had been initially hospitalized, complaining of fever, myalgia, cough, and dyspnea. Electromyogram (EMG) revealed obvious evidence of increased insertional activity (IA) and significant denervation potentials, including positive sharp waves (PSW) and fibrillation potentials, particularly in ankle dorsiflexor muscles. Moreover, no voluntary motor unit action potential (MUAP) was observed. Eventually, the patient was diagnosed with severe axonal mononeuropathy of the right CPN, which could be considered a rare complication of COVID-19.
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Ullah, Khalil, Khalil Khan, Muhammad Amin, Muhammad Attique, Tae-Sun Chung, and Rabia Riaz. "Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters." Applied Sciences 10, no. 15 (July 24, 2020): 5099. http://dx.doi.org/10.3390/app10155099.

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Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.
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Dissertations / Theses on the topic "Motor Unit Action Potentials (MUAP)"

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Vassallo, Carlos Andrés Mugruza. "Modelagem matemática e simulação de potenciais de ação de unidades motoras." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-16112006-174121/.

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Este trabalho apresenta a modelagem matemática e a simulação de potenciais de ação de unidades motoras de músculos de vertebrados visando a posterior simulação do eletromiograma. Para conseguir isso, inicialmente se fez uma compilação de dados existentes para a distribuição das fibras musculares (FBs) nas unidades motoras (MUs) de vários músculos, e as modelagens matemáticas descritos na literatura para o potencial de ação de uma FB (SFAP) e de uma MU (MUAP). Com base nos dados fisiológicos, primeiro se localizou as FBs em um músculo, por meio de uma aproximação de que as FBs estão rodeadas de outras seis no músculo. Para conseguir isto se construiram hexágonos concêntricos por MU, e posteriormente se localizou as FBs nas MUs, cobrindo uma faixa entre 75 e 2000 FBs, o que corresponde a músculos distais de mamíferos. Depois se fez uma aproximação para a distribuição de 170000 FBs nas 272 MUs da cabeça medial do músculo gastrocnêmio (MG) do gato, conseguindo numa primeira simulação localizar cerca de 70% das FBs para cada MU. Com esta localização das FBs no músculo baseados nos dados da literatura se aproximaram os retardos axonais por uma distribuição gaussiana, com média de 2 ms (gato) ou 10 ms (homem) e com desvio padrão de menos de 0,5 ms, desprezando o atraso axonal nas ramificações axonais, que foi estimado no máximo 29 vezes menor. Para a geração do SFAP trabalhou-se com dois modelos, um analítico, o qual resulta em simulações numéricas demoradas, e, outro numérico baseado na convolução da corrente com uma função peso. Para o modelo numérico dobrou-se imaginariamente o comprimento das FBs, para levar em conta o erro computacional de fim de fibra. O modelo numérico resultou em um tempo de simulação 30 vezes menor que o analítico. Adicionalmente, para simular a captação externa (i.e. na pele), fez-se uma aproximação para a função que modela os eletrodos de superfície de secção circular localizados a uma distância maior que 1,79 mm das FBs, mostrando um espectro similar ao reportado na literatura. Finalmente, os MUAPs obtidos resultavam com formas de onda e espectros similares ao descrito na literatura. Além disto, em certos casos, obtiveram-se MUAPs com indentações, seja localizando as junções neuromusculares em bandas da ordem de 1 mm de espessura, seja quando o tempo de atraso axonal foi considerado junto com a velocidade de condução da FB em função da raiz quadrada do diâmetro da FB. Foram feitas simulações para os MG e bíceps braquial do homem. Neste último caso, foram obtidos MUAPs similares aos captados para pessoas saludáveis, e foi observada a freqüência de disparos dos potenciais de ação do motoneurônio no espectro do MUAP. Quanto às formas dos agrupamentos das FBs em uma MU, não se obtiveram diferenças significativas para as FBs posicionadas homogênea e aleatoriamente, exceto uma ligeira variação nas amplitudes. No entanto, ocurreu uma mudança na faixa espectral, quando as FBs estavam concentradas.
This work presents the mathematical model and simulation of motor unit action potentials of vertebrate muscles aiming at after simulation of the electromyogram. To obtain this, initially, it was made a compilation of several data about the distribution of muscle fibers (FBs) in motor units (MUs) of many muscles, and the mathematical models of the action potential of a single FB (SFAP) and MU (MUAP), reported in previous works. On the basis of this physiological data, first, the FB was located in a muscle, using an approximation in which the FBs are encircled with other six FBs in the muscle. To reach this, concentric hexagons were constructed to build the surface of the MU, and later the FBs were situated in the MU, covering a range between 75 and 2000 FBs, corresponding to mammals extremity muscles. Later, a new approximation were was madein order to distribute the 170000 FBs in the 272 MUs of the medial head of muscle medialis gastrocnemius (MG) of the cat, reaching, in a first simulation, the localization of almost 70% of the FBs at each MU. With the FBs lalready allocated in the muscle, and based in data of previous works, their axonal delay were approximated by a gaussian distribution, with mean of 2 ms (cat) or 10 ms (man) and standard deviation of less than 0,5 ms, discarding the axonal delay in the axonal branching, that were estimated to affectup to 29 times less. To SFAP generation, two models were used, the first analytical, resulting in delayed numerical simulations, and the other based on convolution of the second derivate of the current with a weight function, where the length of the FBs was imaginarily duplicated, in order to consider the end fiber effect. Using this, a simulation time 30 times lesser than the analytical one was obtained. Additionally, so as to simulate the external recording (i.e. in the skin), it was made an approximation to the function that models the circular shape surface electrodes located at distances greater than 1,79 mm of the FBs, showing a similar spectrum reported. Finally, the waves and spectrum of the simulated MUAPs resulted similar to the ones reported in the literature. Beyond this, in certain cases, MUAPs were simulated with some tuned, either locating the neuromuscular junctions with thickness bands of 1 mm, or, when the axonal delay and the FB muscle fiber conduction velocity were considered as a function of the square root fiber diameter. This was simulated for MUAPs of MG and biceps brachii muscles of human beings, in the last case it has reached the waveforms and tuned found in heath subjects, and it was visualized the mean frequency of firing rate at the spectrum. In order to know how much affects grouping for the FBs to waves a MU, they were not found significant differences with FBs located homogeneously and randomly, except a little variation in the amplitude of the MUAP. However, they presented a change in the spectral bandwidth when the FBs are more concentrated.
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Naik, Ganesh Ramachandra, and ganesh naik@rmit edu au. "Iterative issues of ICA, quality of separation and number of sources: a study for biosignal applications." RMIT University. Electrical and Computer Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090320.115103.

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This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.
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Twardowski, Michael D. "Deriving Motor Unit-based Control Signals for Multi-Degree-of-Freedom Neural Interfaces." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-dissertations/601.

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Beginning with the introduction of electrically powered prostheses more than 65 years ago surface electromyographic (sEMG) signals recorded from residual muscles in amputated limbs have served as the primary source of upper-limb myoelectric prosthetic control. The majority of these devices use one or more neural interfaces to translate the sEMG signal amplitude into voltage control signals that drive the mechanical components of a prosthesis. In so doing, users are able to directly control the speed and direction of prosthetic actuation by varying the level of muscle activation and the associated sEMG signal amplitude. Consequently, in spite of decades of development, myoelectric prostheses are prone to highly variable functional control, leading to a relatively high-incidence of prosthetic abandonment among 23-35% of upper-limb amputees. Efforts to improve prosthetic control in recent years have led to the development and commercialization of neural interfaces that employ pattern recognition of sEMG signals recorded from multiple locations on a residual limb to map different intended movements. But while these advanced algorithms have made strident gains, there still exists substantial need for further improvement to increase the reliability of pattern recognition control solutions amongst the variability of muscle co-activation intensities. In efforts to enrich the control signals that form the basis for myoelectric control, I have been developing advanced algorithms as part of a next generation neural interface research and development, referred to as Motor Unit Drive (MU Drive), that is able to non-invasively extract the firings of individual motor units (MUs) from sEMG signals in real-time and translate the firings into smooth biomechanically informed control signals. These measurements of motor unit firing rates and recruitment naturally provide high-levels of motor control information from the peripheral nervous system for intact limbs and therefore hold the greater promise for restoring function for amputees. The goal for my doctoral work was to develop advanced algorithms for the MU Drive neural interface system, that leverage MU features to provide intuitive control of multiple degrees-of-freedom. To achieve this goal, I targeted 3 research aims: 1) Derive real-time MU-based control signals from motor unit firings, 2) Evaluate feasibility of motor unit action potential (MUAP) based discrimination of muscle intent 3) Design and evaluate MUAP-based motion Classification of motions of the arm and hand.
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Grönlund, Christer. "Spatio-temporal processing of surface electromyographic signals : information on neuromuscular function and control." Doctoral thesis, Umeå universitet, Institutionen för strålningsvetenskaper, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-958.

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During muscle contraction, electrical signals are generated by the muscle cells. The analysis of those signals is called electromyography (EMG). The EMG signal is mainly determined by physiological factors including so called central factors (central nervous system origin) and peripheral factors (muscle tissue origin). In addition, during the acquisition of EMG signals, technical factors are introduced (measurement equipment origin). The aim of this dissertation was to develop and evaluate methods to estimate physiological properties of the muscles using multichannel surface EMG (MCsEMG) signals. In order to obtain accurate physiological estimates, a method for automatic signal quality estimation was developed. The method’s performance was evaluated using visually classified signals, and the results demonstrated high classification accuracy. A method for estimation of the muscle fibre conduction velocity (MFCV) and the muscle fibre orientation (MFO) was developed. The method was evaluated with synthetic signals and demonstrated high estimation precision at low contraction levels. In order to discriminate between the estimates of MFCV and MFO belonging to single or populations of motor units (MUs), density regions of so called spatial distributions were examined. This method was applied in a study of the trapezius muscle and demonstrated spatial separation of MFCV (as well as MFO) even at high contraction levels. In addition, a method for quantification of MU synchronisation was developed. The performance on synthetic sEMG signals showed high sensitivity on MU synchronisation and robustness to changes in MFCV. The method was applied in a study of the biceps brachii muscle and the relation to force tremor during fatigue. The results showed that MU synchronisation accounted for about 40 % of the force tremor. In conclusion, new sEMG methods were developed to study muscle function and motor control in terms of muscle architecture, muscle fibre characteristics, and processes within the central nervous system.
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Books on the topic "Motor Unit Action Potentials (MUAP)"

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Kennett, Robin P., and Sidra Aurangzeb. Primary muscle diseases. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0024.

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This chapter on primary muscle diseases explains how analysis of compound muscle action potential (CMAP) amplitude, abnormal spontaneous activity on needle electromyography (EMG), and motor unit action potentials (MUAP) characteristics may be used to give an indication of pathophysiological processes, and goes on to describe the combination and distribution of abnormalities that may be expected in the more commonly encountered myopathies. The conditions considered in detail are inflammatory myopathy (including myositis), critical illness myopathy, disorders with myotonia, inherited myopathy (including muscular dystrophy), and endocrine, metabolic and toxic disorders. Each of these has a characteristic combination of CMAP, spontaneous EMG, and MUAP findings, but the systematic approach to clinical neurophysiology as a way of understanding muscle pathophysiology can be used to investigate the myriad of rare myopathies that may be encountered in clinical practice.
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Katirji, Bashar. Case 20. Edited by Bashar Katirji. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190603434.003.0024.

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Inflammatory myopathies are a group of disorders characterized by necrotizing myopathies with inflammatory infiltrates. Dermatomyositis, polymyositis, and inclusion body myositis are the classical types although other overlapping disorders are now more commonly diagnosed, including necrotizing autoimmune myopathy and the anti-synthetase syndrome. This case presents a typical patient with polymyositis and outlines the clinical and pathological findings of the various inflammatory myopathies. It highlights the differential diagnosis as well as the differences and similarities among the autoimmune inflammatory myopathies. A detailed discussion of the needle electromyography findings in the various myopathies is included: Myopathies are classified into those with or without fibrillations, with or without myotonic discharges, and with or without changes in the morphology of the motor unit action potentials.
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Book chapters on the topic "Motor Unit Action Potentials (MUAP)"

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Barbero, Marco, Roberto Merletti, and Alberto Rainoldi. "Generation, Propagation, and Extinction of Single-Fiber and Motor Unit Action Potentials." In Atlas of Muscle Innervation Zones, 21–38. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2463-2_3.

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Masuda, T., H. Endo, T. Kumagai, and T. Takeda. "Magnetic Recording of the Propagation of Motor Unit Action Potentials in the Human Leg Muscles." In Biomag 96, 825–28. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1260-7_202.

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Holobar, Aleš. "Decomposition of Compound Muscle Action Potentials by Convolution Kernel Compensation Method: Improved Segmentation of Motor Unit Firings." In 8th European Medical and Biological Engineering Conference, 324–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64610-3_38.

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Sharma, Rishi Raj, Mohit Kumar, and Ram Bilas Pachori. "Classification of EMG Signals Using Eigenvalue Decomposition-Based Time-Frequency Representation." In Biomedical and Clinical Engineering for Healthcare Advancement, 96–118. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0326-3.ch006.

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Electromyogram (EMG) signals are commonly used by doctors to diagnose abnormality of muscles. Manual analysis of EMG signals is a time-consuming and cumbersome task. Hence, this chapter aims to develop an automated method to detect abnormal EMG signals. First, authors have applied the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT) method to obtain the time-frequency (TF) representation of motor unit action potentials (MUAPs) extracted from EMG signals. Then, the obtained TF matrices are used for features extraction. TF matrix has been sliced into several parts and fractional energy in each slice is computed. A percentile-based slicing is applied to obtain discriminating features. Finally, the features are used as an input to the classifiers such as random forest, least-squares support vector machine, and multilayer perceptron to classify the EMG signals namely, normal and ALS, normal and myopathy, and ALS and myopathy, and achieved accuracy of 83%, 80.8%, and 96.7%, respectively.
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"Atlas of Motor Unit Action Potentials." In Practical Approach to Electromyography. New York, NY: Springer Publishing Company, 2011. http://dx.doi.org/10.1891/9781617050053.0015.

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MYERS, STANLEY J., and ROBERT E. LOVELACE. "The Motor Unit and Muscle Action Potentials." In The Physiological Basis of Rehabilitation Medicine, 243–82. Elsevier, 1994. http://dx.doi.org/10.1016/b978-1-4831-7818-9.50017-4.

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Sorenson, Eric J., and Jasper R. Daube. "Quantitative Motor Unit Number Estimates." In Clinical Neurophysiology, 361–81. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190259631.003.0022.

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Quantitative motor unit number estimates (MUNE) quantify the number of viable motor axons that innervate a muscle or muscle group. Various techniques have been developed to accomplish this, but no single technique has demonstrated superiority. The MUNE is calculated from the supramaximal compound muscle action potential (CMAP) by dividing the CMAP by the mean size of the motor unit potentials. The resulting unit-less number represents the number of motor units within a muscle or muscle group. MUNE has been applied most widely to disorders of the motor neuron such as ALS, spinal muscular atrophy, and polio, and it has been used in animal and human studies, and as an outcome measure in clinical trials for ALS and spinal muscular atrophy. Because of the limitations of MUNE (including patient discomfort, prolonged time to perform, and technical expertise for reliable results), it has not entered into routine clinical practice at this time.
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Arasaki, Keisuke. "MUNE by intraneural microstimulation and the effects of averaging of unitary muscle action potentials." In Motor Unit Number Estimation (MUNE): Proceedings of the First International Symposium on MUNE, 46–50. Elsevier, 2003. http://dx.doi.org/10.1016/s1567-424x(02)55006-6.

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Conference papers on the topic "Motor Unit Action Potentials (MUAP)"

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Pham, Thuy T., Andrew J. Fuglevand, Alistair L. McEwan, and Philip H. W. Leong. "Unsupervised discrimination of motor unit action potentials using spectrograms." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6943514.

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Pham, Thuy T., Diep N. Nguyen, Eryk Dutkiewicz, Alistair L. McEwan, Philip H. W. Leong, and Andrew J. Fuglevand. "Feature Analysis for Discrimination of Motor Unit Action Potentials." In 2018 12th International Symposium on Medical Information and Communication Technology (ISMICT). IEEE, 2018. http://dx.doi.org/10.1109/ismict.2018.8573738.

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Elia, Pattichis, Fincham, Spanias, and Mlddleton. "Autoregressive Spectral Modeling Of Motor Unit Action Potentials: Preliminary Findings." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.589516.

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Elia, A., C. Pattichis, W. Fincham, A. Spanias, and L. Middleton. "Autoregressive spectral modeling of Motor Unit Action Potentials: Preliminary findings." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5761878.

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Dobrowolski, Andrzej P., Mariusz Wierzbowski, and Kazimierz Tomczykiewicz. "Wavelet analysis for Support Vector Machine classification of motor unit action potentials." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5626480.

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Malanda, Armando, Ignacio Rodríguez, Luis Gila, Iñaki García-Gurtubay, Javier Navallas, and Javier Rodríguez. "Correlation-based Method for Measuring the Duration of Motor Unit Action Potentials." In 9th International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005648301290136.

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Sedghamiz, Hooman, and Daniele Santonocito. "Unsupervised detection and classification of motor unit action potentials in intramuscular electromyography signals." In 2015 E-Health and Bioengineering Conference (EHB). IEEE, 2015. http://dx.doi.org/10.1109/ehb.2015.7391510.

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Marquez L., Alejandro P., Alfredo Ramerez-Garcia, and Roberto Munoz G. "Algorithm for identification of motor unit action potentials based on wavelet transform and neural networks." In 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) (Formerly known as ICEEE). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5608667.

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Xiaomei Ren, Zhizhong Wang, and Xiao Hu. "Independent Component Analysis and Wavelet Decomposition Technique for the Detection of Motor Unit Action Potentials." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1617024.

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Kitov, Vladimir, Elena Tomilovskaya, and Tatiana Shigueva. "SEMI-AUTOMATIC ALGORITHM FOR MOTOR UNIT ACTION POTENTIALS RECOGNITION. EFFECT OF DRY IMMERSION ON CALF MUSCLES MOTOR UNITS RECRUITMENT ORDRER." In XV International interdisciplinary congress "Neuroscience for Medicine and Psychology". LLC MAKS Press, 2019. http://dx.doi.org/10.29003/m420.sudak.ns2019-15/215.

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