Techniques and applications of EMG: measuring motor units from structure to function

Thornton, Rachel; Michell, Andrew W.

doi:10.1007/s00415-011-6350-0

Cited by 16 publications

(10 citation statements)

References 50 publications

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“…Decomposition is useful in a wide range of clinical and scientific studies of the neuromuscular system (for reviews, see [4]- [6]). For most decomposition-based studies, an automated algorithm is utilized to perform most of the decomposition, with expert manual editing often completed thereafter.…”

Section: Introductionmentioning

confidence: 99%

Cross-Comparison of Three Electromyogram Decomposition Algorithms Assessed With Experimental and Simulated Data

Dai

Christie

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

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The reliability of clinical and scientific information provided by algorithms that automatically decompose the electromyogram (EMG) depends on the algorithms' accuracies. We used experimental and simulated data to assess the agreement and accuracy of three publicly available decomposition algorithms-EMGlab (McGill , 2005) (single channel data only), Fuzzy Expert (Erim and Lim, 2008) and Montreal (Florestal , 2009). Data consisted of quadrifilar needle EMGs from the tibialis anterior of 12 subjects at 10%, 20% and 50% maximum voluntary contraction (MVC); single channel needle EMGs from the biceps brachii of 10 controls and 10 patients during contractions just above threshold; and matched simulated data. Performance was assessed via agreement between pairs of algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For the quadrifilar experimental data, median agreements between the Montreal and Fuzzy Expert algorithms at 10%, 20%, and 50% MVC were 95%, 86%, and 64%, respectively. For the single channel control and patient data, median agreements between the three algorithm pairs were statistically similar at ∼ 97% and ∼ 92%, respectively. Accuracy on the simulated data exceeded this performance. Agreement/accuracy was strongly related to the Decomposability Index (Florestal , 2009). When agreement was high between algorithm pairs applied to simulated data, so was accuracy.

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Section: Introductionmentioning

confidence: 99%

Cross-Comparison of Three Electromyogram Decomposition Algorithms Assessed With Experimental and Simulated Data

Dai

Christie

et al. 2015

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Conversely, overlapped MUAP waveforms are created by the partial or full superposition of two or more single MUAPs discharging simultaneously, making the constituent waveforms much more difficult to parse. According to Thornton and Michell ( 27 ), MUAPs from a healthy musculature may contain up to four phases while an increase in MUAP phases may be indicative of the MU remodeling period that occurs after pathological denervation. In this study, the subject-specific recognition of isolated/overlapped MUAPs was carried out by assigning a tetraphasic (4-phase) template to the EMG signals from healthy participants and a hexaphasic (6-phase) template to the signals from stroke participants.…”

Section: Methodsmentioning

confidence: 99%

Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution

Ren

Zhang

et al. 2018

Front. Neurol.

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A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-off approach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients.

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“…Doing so permits the evaluation and study of individual motor unit (MU) firing patterns and action potential shapes, which is useful in a wide range of clinical and scientific studies (for reviews, see [4]- [6]). In most decomposition schemes, an automated algorithm detects and clusters each MUAP firing, typically with expert manual editing performed thereafter.…”

Section: Introductionmentioning

confidence: 99%

Performance of three electromyogram decomposition algorithms as a function of signal to noise ratio: Assessment with experimental and simulated data

Dai

Clancy

et al. 2014

2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

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We have previously published a full report [25] comparing the performance of three automated electromyogram (EMG) decomposition algorithms. In our prior report, the primary measure of decomposition difficulty/challenge for each data record was the "Decomposability Index" of Florestal et al.[3]. This conference paper is intended to augment our prior work by providing companion results when the measure of difficulty is the motor unit signal-to-noise ratio (SNR MU ) -a measure that is commonly used in the literature. Thus, we analyzed experimental and simulated data to assess the agreement and accuracy, as a function of SNR MU , of three publicly available decomposition algorithms-EMGlab [1] (single channel data only), Fuzzy Expert [2] and Montreal [3]. Data consisted of quadrifilar needle EMGs from the tibialis anterior of 12 subjects at 10%, 20% and 50% maximum voluntary contraction (MVC); single channel needle EMGs from the biceps brachii of 10 control subjects during contractions just above threshold; and matched simulated data. Performance vs. SNR MU was assessed via agreement between pairs of algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For experimental data, RMS errors between the achieved agreement and those predicted by an exponential model as a function of SNR MU ranged from 8.4% to 19.2%. For the simulations, RMS errors between achieved accuracy and those predicted by the SNR MU exponential model ranged from 3.7% to 14.7%. Agreement/accuracy was strongly related to SNR MU .

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Techniques and applications of EMG: measuring motor units from structure to function

Cited by 16 publications

References 50 publications

Cross-Comparison of Three Electromyogram Decomposition Algorithms Assessed With Experimental and Simulated Data

Cross-Comparison of Three Electromyogram Decomposition Algorithms Assessed With Experimental and Simulated Data

Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution

Performance of three electromyogram decomposition algorithms as a function of signal to noise ratio: Assessment with experimental and simulated data

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