Decomposition-based quantitative electromyography: Effect of force on motor unit potentials and motor unit number estimates (original) (raw)
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Muscle & Nerve, 2003
Quantitative electromyographic (EMG) techniques provide clinically useful information to aid in the diagnosis and follow the course or response to treatment of diseases affecting the motor system. The purpose of this study was to describe a decomposition-based quantitative electromyography method (DQEMG) designed to obtain clinically applicable information relating to motor unit potential (MUP) size and configuration, and motor unit (MU) firing characteristics. Additionally, preliminary normative data were obtained from the deltoid, biceps brachii, first dorsal interosseous, vastus medialis, and tibialis anterior muscles of 13 control subjects. DQEMG was capable of efficiently and accurately extracting MUP data from complex interference patterns during mild to moderate contractions. MUP amplitude, surface-detected MUP (S-MUP) amplitude, MUP duration, number of phases, and MU firing frequencies varied significantly across muscles. The mean parameter values for the individual muscles studied were similar to previous reports based on other quantitative methods. The main advantages of this method are the speed of data acquisition and processing, the ability to obtain MUPs from MUs with low and higher recruitment thresholds, and the ability to obtain both S-MUP or macro-MUP data as well as MU firing rate information.
Clinical Neurophysiology, 2006
Objective: To establish within-subject reliability of motor unit number estimates (MUNEs) and quantitative MU analysis using decomposition-based quantitative electromyography (DQEMG). Methods: Following the acquisition of a maximum M-wave, needle and surface-detected EMG signals were collected during contractions of the first dorsal interrosseous (FDI) and biceps brachii (BB). DQEMG was used to extract motor unit potential (MUP) trains and surfacedetected MUPs associated with each train, the mean size of which was divided into the maximum M-wave to obtain a MUNE. Retests were performed following the initial test to evaluate reliability. Results: Subjects test-retest MUNEs were highly correlated (rZ0.72 FDI; 0.97 BB) with no significant differences between test and retest MUNE values (PO0.10). Ninety-five percent confidence intervals were calculated to establish the range of expected retest MUNE variability and were G41 MUs for the FDI and BB. Quantitative information pertaining to MU size, complexity and firing rate were similar for both tests. Conclusion: MUNEs and quantitative MU data can be obtained reliably from the BB and FDI using DQEMG in individual subjects. Significance: Establishing within-subject reliability of MUNEs and quantitative MU analysis allow clinicians to longitudinally follow changes in the MU pool of individuals with disorders of the central or peripheral nervous system in addition to assessing their response to treatments.
Journal of Neural Engineering, 2014
Objective. The aim of this study is to assess the accuracy of a surface electromyogram (sEMG) motor unit (MU) decomposition algorithm during low levels of muscle contraction. Approach. A two-source method was used to verify the accuracy of the sEMG decomposition system, by utilizing simultaneous intramuscular and surface EMG recordings from the human first dorsal interosseous muscle recorded during isometric trapezoidal force contractions. Spike trains from each recording type were decomposed independently utilizing two different algorithms, EMGlab and dEMG decomposition algorithms. The degree of agreement of the decomposed spike timings was assessed for three different segments of the EMG signals, corresponding to specified regions in the force task. A regression analysis was performed to examine whether certain properties of the sEMG and force signal can predict the decomposition accuracy. Main results. The average accuracy of successful decomposition among the 119 MUs that were common to both intramuscular and surface records was approximately 95%, and the accuracy was comparable between the different segments of the sEMG signals (i.e., force ramp-up versus steady state force versus combined). The regression function between the accuracy and properties of sEMG and force signals revealed that the signal-to-noise ratio of the action potential and stability in the action potential records were significant predictors of the surface decomposition accuracy. Significance. The outcomes of our study confirm the accuracy of the sEMG decomposition algorithm during low muscle contraction levels and provide confidence in the overall validity of the surface dEMG decomposition algorithm.
Journal of Electromyography and Kinesiology, 2006
The aim of the study was to investigate amplitude and frequency content of single motor unit (MU) electromyographic (EMG) and mechanomyographic (MMG) responses. Multi-channel surface EMG and MMG signals were detected from the dominant biceps brachii muscle of 10 volunteers during isometric voluntary contractions at 20%, 50%, and 80% of the maximal voluntary contraction (MVC) force. Each contraction was performed three times in the experimental session which was repeated in three non-consecutive days. Single MU action potentials were identified from the surface EMG signals and their times of occurrence used to trigger the averaging of the MMG signal. At each contraction level, the MUs with action potentials of highest amplitude were identified. Single MU EMG and MMG amplitude and mean frequency were estimated with normalized standard error of the mean within subjects (due to repetition of the measure in different trials and experimental sessions) smaller than 15% and 7%, respectively, in all conditions. The amplitude of the action potentials of the detected MUs increased with increasing force (mean ± SD, 244 ± 116 lV at 20% MVC, and 1426 ± 638 lV at 80% MVC; P < 0.001) while MU MMG amplitude increased from 20% to 50% MVC (40.5 ± 20.9 and 150 ± 88.4 mm/s 2 , respectively; P < 0.001) and did not change significantly between 50% and 80% MVC (129 ± 82.7 mm/s 2 at 80% MVC). MU EMG mean frequency decreased with contraction level (20% MVC: 97.2 ± 13.9 Hz; 80% MVC: 86.2 ± 11.4 Hz; P < 0.001) while MU MMG mean frequency increased (20% MVC: 33.2 ± 6.8 Hz; 80% MVC: 40.1 ± 6.1 Hz; P < 0.001). EMG peak-to-peak amplitude and mean frequency of individual MUs were not correlated with the corresponding variables of MMG at any contraction level.
Electroencephalography and clinical neurophysiology, 1995
In this prospective study, automated interference pattern analysis (IPA, "Willison analysis", modified by Stålberg et al. 1983) was compared to the quantitative evaluation of mean motor unit potential duration (QMUP) in 239 muscles from consecutive, unselected patients. The sensitivity and specificity of both methods were calculated with respect to the clinically derived final neurological diagnosis, with histology available for 120 examinations. Whereas specificities were not different for the methods, the sensitivity for detection of abnormal vs. normal was 49% for QMUP and 74% for IPA (P < 0.001). The sensitivity for detection of myopathy or neuropathy was 46% or 38% for QMUP and 75% (P < 0.001) or 53% (P < 0.05) for IPA. Thus, in all instances, IPA had superior sensitivity with unchanged specificity as compared to QMUP. The results of a rapid and purely qualitative visual MUP assessment were statistically not different from QMUP. Although widely used, neither ...
Clinical Neurophysiology, 2009
Objective: To establish the inter-rater reliability of decomposition-based quantitative electromyography (DQEMG) derived motor unit number estimates (MUNEs) and quantitative motor unit (MU) analysis. Methods: Using DQEMG, two examiners independently obtained a sample of needle and surface-detected motor unit potentials (MUPs) from the tibialis anterior muscle from 10 subjects. Coupled with a maximal M wave, surface-detected MUPs were used to derive a MUNE for each subject and each examiner. Additionally, size-related parameters of the individual MUs were obtained following quantitative MUP analysis. Results: Test-retest MUNE values were similar with high reliability observed between examiners (ICC = 0.87). Additionally, MUNE variability from test-retest as quantified by a 95% confidence interval was relatively low (AE28 MUs). Lastly, quantitative data pertaining to MU size, complexity and firing rate were similar between examiners. Conclusion: MUNEs and quantitative MU data can be obtained with high reliability by two independent examiners using DQEMG. Significance: Establishing the inter-rater reliability of MUNEs and quantitative MU analysis using DQEMG is central to the clinical applicability of the technique. In addition to assessing response to treatments over time, multiple clinicians may be involved in the longitudinal assessment of the MU pool of individuals with disorders of the central or peripheral nervous system.
Journal of Electromyography and Kinesiology, 2008
Surface electromyography parameters such as root-mean-square value (RMS) and median power frequency (FMED) are commonly used to assess the input of the central nervous system (CNS) to a muscle. However, RMS and FMED are influenced not only by CNS input, but also by peripheral muscle properties. The number of motor unit action potentials (MUAPs) per second, or MUAP Rate (MR), being the sum of the firing rates of the active motor units, would reflect CNS input solely. This study explored MR behaviour in relation to force and during a fatiguing contraction in comparison to RMS and FMED.
Journal of Electromyography and Kinesiology, 2005
Changes in muscle fibre length and surface electrode position with respect to the muscle fibres affect the amplitude and frequency characteristics of surface electromyography (SEMG) in different ways. Knowledge of changes in muscle fibre length would help towards a better interpretation of the signals. The possibility of estimating the length through SEMG during voluntary contractions was checked in this study. The fibresÕ semi-length was estimated from the product of the conduction velocity and conduction time during which the wave of excitation propagated from the end-plate region to the ends of the fibres. Short (10 s), moderate (30% of maximum voluntary contraction) isometric contractions were performed by 10 subjects at different elbow joint angles (80-140°in steps of 20°). Monopolar signals were detected non-invasively, using a two-dimensional electrode array. High spatial resolution EMG and a decomposition technique were utilised to extract single motor unit activities for triggered averaging and to estimate conduction velocity. A significant increase with joint angle was found in conduction time and estimated fibre semi-length. Changes in conduction velocity with joint angle were found to be not significant. The methodology described allows the relative changes in fibresÕ semi-length to be estimated from SEMG data.
Motor unit size estimation of enlarged motor units with surface electromyography
Muscle & Nerve, 1998
Surface EMG is hardly used to estimate motor unit (MU) characteristics, while its non-invasiveness is less stressful for patients and allows multi-electrode recordings to investigate different sites of the muscle and MU. The present study compares motor unit potentials (MUPs) obtained with surface EMG and macro EMG during voluntary contraction of the biceps brachii muscle of patients with enlarged MUs caused by prior poliomyelitis. Averaged surface MUPs were obtained by means of needle EMG (SMUP1) and surface EMG (SMUP2) triggering. The MUPs area and peak amplitudes correlated well when comparing the macro MUP and SMUP1 of the same MUs. When MU populations of different patients were compared, the SMUP1s and SMUP2s were equally sensitive to pathology as macro MUPs. In this, the late non-propagating positive wave (only present in unipolar recordings) is more robust than the triphasic propagating wave. Therefore, surface EMG can be used for detecting enlarged MUs.