Variability of some SEMG parameter estimates with electrode location

Hogrel, Jean‐Yves; Duchêne, Jacques; Marini, Jean-François

doi:10.1016/s1050-6411(97)00043-6

Cited by 86 publications

(92 citation statements)

References 30 publications

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“…As a result, the terminal phases are especially pronounced in signals detected from deeper muscles (5,6,30), which makes these phases related to the cross talk (5,6,30). (7) The regions near the end-plate and fiber ends are inappropriate for noninvasive estimation of propagation velocity (16,(26)(27)(28).…”

Section: Discussionmentioning

confidence: 99%

Electromyography (EMG) Modeling

Dimitrova

Dimitrov

2006

Wiley Encyclopedia of Biomedical Engineering

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Modeling is a measure of our understanding of processes and a tool for checking hypotheses and making prognoses. Any attempt to understand and explain electromyographic (EMG) signals, their characteristics, and changes is related to modeling. The authors’ aim is to present the main ideas of EMG modeling in terms understandable to people without special mathematical education. The main physical regularities that rule the formation of electrical potentials in a volume conductor (extracellular medium) are pictorially demonstrated and can be realized ignoring equations. Potentials produced by individual muscle fibers are the basis of EMG signals, which is why a more detailed picture of the physical basis for modeling single‐fiber potentials (SFPs) generated in a homogeneous volume conductor of infinite extent is presented. It is demonstrated why the shape of SFP changes with distance from the active fibers, why amplitude of EMG signal can be an unreliable characteristic, and why detection of EMG by needle and surface electrodes has different sensitivity to prolonged phase of repolarization or to after‐depolarization in intracellular action potential (IAP) and to IAP propagation velocity along the fiber. Description of some equations resulting in fast calculation of the motor unit potentials and interference EMG are presented. The latest papers on mathematical modeling that take into account inhomogeneous and restricted muscle tissue are discussed.

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Section: Discussionmentioning

confidence: 99%

Electromyography (EMG) Modeling

Dimitrova

Dimitrov

2006

Wiley Encyclopedia of Biomedical Engineering

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“…As has been shown in various experimental (HERMENS and FRERIKS, 1997;HOGREL et al, 1998;JENSEN et al, 1993;LI and SAKAMOTO, 1996;Roy et al, 1986;ZUN~GA and SIMONS, 1970) and simulation (LATEVA et al,1993;DIMITROV and DIMITROVA, 1998;DIMITROVA et al, 2001;FARINA et al, 2002a) studies, the properties of EMG signals depend on the placement of the electrodes relative to the innervation zones and fibre-tendon junctions. Sliding of the fibres with respect to the detection electrodes can thus considerably affect EMG features .…”

Section: Introductionmentioning

confidence: 88%

Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals

Schulte¹,

Farina

Merletti

et al. 2004

Med. Biol. Eng. Comput.

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The study of surface electromyographic (EMG) signals under dynamic contractions is becoming increasingly important. However, knowledge of the methodological issues that may affect such analysis is still limited. The aim of the study was to analyse the effect of fibre shortening on estimates of conduction velocity (CV) and mean power spectral frequency (MNF) from surface EMG signals. Single fibre action potentials were simulated, as detected by commonly used spatial filters, for different fibre lengths. No physiological modifications were included with changes in fibre length, and thus only geometrical artifacts related to fibre shortening were investigated. The simulation results showed that the dependence of CV and MNF on fibre shortening is affected by the fibre location, electrode position and the spatial filter applied. With shortening of up to 50% for a fibre of 50 mm semi-length, the variations in CV and MNF estimates with shortening in bipolar recordings were 0.5% (CV) and 0.7% (MNF) for superficial fibres, and 3.6% and 5.1% for deeper fibres. Using the longitudinal double differential filter, under the same conditions, the percent variation was 0% and 0.2%, and 24.7% and 15.8%, respectively. The main conclusions were, first, muscle fibre shortening can significantly affect estimates of CV and MNF, especially for short fibre lengths. However, for long (semi-length >50 mm) and superficial fibres, this effect is limited for shortenings of up to 50% of the initial fibre length. Secondly, CV and MNF are almost equally affected by changes in muscle length; and, thirdly, sensitivity to fibre shortening depends on the spatial filter applied for signal detection.

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“…This estimation can be more or less biased, depending on the experimental conditions (e.g. electrode configuration and localisation (HOGREL et al, 1998)) and processing methods. Nevertheless, all estimates of mean MFCV seem to present an acceptable reliability with respect to variance and sensitivity to noise.…”

Section: Introductionmentioning

confidence: 99%

Motor unit conduction velocity distribution estimation: Assessment of two short-term processing methods

Hogrel

Duchêne

2002

Med Bio Eng Comput

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Muscle fibre conduction velocity (MFCV) can be used as an index of the structural and/or functional modifications that can occur during fatigue or pathological processes. Current evaluation of MFCV from surface electromyography (SEMG) classically produces an average value. However, a single mean value is not sufficient when modifications affect only a small part of the conduction velocity distribution. In such a case, an estimation of the whole motor unit conduction velocity distribution (MUCV) would be advantageous. The aim of this study was the evaluation of the quality of two short-term methods based on cross-correlation (CC) and peak-to-peak (PP) estimation. A comprehensive simulation program was used to generate signals with known MUCV distributions. The Dmax statistic of Kolmogorov-Smirnov was used as an error criterion to quantify the estimation error and to optimise the MUCV distribution computation algorithms. The minimum error was observed for an analysing window of 10ms for PP and 15ms for CC. Dmax was significantly lower for PP (0.195+/-0.054) than for CC (0.343+/-0.073). Various simulations showed the strong effect of the variance of the true distribution on the features of the estimated ones. Clinical data measured on the abductor pollicis brevis were studied. MUCV was estimated on a healthy subject (3.63+/-0.87ms(-1)), a patient suffering from a myopathy (2.73+/-0.51ms(-1)) and one suffering from a neuropathy (4.38+/-0.23ms(-1)). The results demonstrate the overall superiority of a peak-to-peak approach.

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Variability of some SEMG parameter estimates with electrode location

Cited by 86 publications

References 30 publications

Electromyography (EMG) Modeling

Electromyography (EMG) Modeling

Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals

Motor unit conduction velocity distribution estimation: Assessment of two short-term processing methods

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