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Biomedical subjects

D Stashuk

Publications and source records attributed to D Stashuk.

10 recordsLinked to original sources
PubMed1999

The relationship of motor unit size, firing rate and force.

OBJECTIVE: Using a clinical electromyographic (EMG) protocol, motor units were sampled from the quadriceps femoris during isometric contractions at fixed force levels to examine how average motor unit size and firing rate relate to force generation. METHODS: Mean firing rates (mFRs) and sizes (mean surface-detected motor unit action potential (mS-MUAP) area) of samples of active motor units were assessed at various force levels in 79 subjects. RESULTS: MS-MUAP size increased linearly with increased force generation, while mFR remained relatively constant up to 30% of a maximal force and increased appreciably only at higher force levels. A relationship was found between muscle force and mS-MUAP area (r2 = 0.67), mFR (r2 = 0.38), and the product of mS-MUAP area and mFR (mS-MUAP x mFR) (r2 = 0.70). CONCLUSIONS: The results support the hypothesis that motor units are recruited in an orderly manner during forceful contractions, and that in large muscles only at higher levels of contraction ( > 30% MVC) do mFRs increase appreciably. MS-MUAP and mFR can be assessed using clinical EMG techniques and they may provide a physiological basis for analyzing the role of motor units during muscle force generation.

Action Potentials↗
PubMed1998

Firing rate analysis using decompostion-enhanced spike triggered averaging in the quadriceps femoris.

Electromyographic signals detected from the quadriceps femoris during various constant force contractions were decomposed to identify individual motor unit discharges and mean firing rates (FRs). Subject and group mean FRs were calculated for each force level. Mean FR values and FR variability increased with force. Individual, subject, and group mean FRs showed slight increases until 30% of maximum voluntary contraction and larger increases thereafter. Findings are discussed in relation to motor unit recruitment, frequency modulation, and fatigue.

Action Potentials↗
PubMed1998

Robust supervised classification of motor unit action potentials.

A certainty-based classification algorithm is described, which comprises part of a clinically used EMG signal decomposition system. This algorithm classifies a candidate motor unit action potential (MUAP) to the motor unit potential trian (MUAPT) that produces the greatest estimated certainty, provided this maximal certainty is above a given threshold. The algorithm is iterative, such that the certainty with which assignments are made increases with each pass through the data, and it has specific stopping criteria. The performance and sensitivity (to the assignment threshold) of the Certainty algorithm and an iterative minimum Euclidean distance (MED) algorithm are compared by classifying sets of MUAPs detected in real concentric needle-detected EMG signals, using a range of assignment thresholds for each algorithm. With regard to MUAP assignment and error rates, the Certainty algorithm consistently provides better mean results and, more importantly, less variable results than the MED algorithm. The Certainty algorithm can provide mean assignment and error rates of 80.8 and 1.5%, respectively, with a maximum error rate of 3.2%; the MED algorithm can provide mean assignment and error rates of 80.3 and 3.3%, respectively, with a maximum error rate of 6.5%. The Certainty algorithm is relatively insensitive to the certainty threshold used, can consistently differentiate between similarly shaped MUAPs from different MUAPTs, and can make correct classifications despite biological shape variability, background noise and signal shape nonstationarity.

Algorithms↗
PubMed1997

Decomposition-enhanced spike-triggered averaging: contraction level effects.

Decomposition-enhanced spike-triggered averaging (DE-STA) was applied to the vastus medialis muscle to examine size distributions of surface-detected motor-unit action potentials (S-MUAPs) at various force levels. Using DE-STA, 15-20 S-MUAPs were identified during 5%, 10%, 20%, and 30% of maximum voluntary contraction. Average S-MUAPs showed increase in peak to peak (and negative peak) amplitude with force (In microV): 5% = 37.9 +/- 6.1 (16.6 +/- 2.5), 10% = 44.0 +/- 4.0 (20.4 +/- 1.8), 20% = 80.7 +/- 9.3 (41.3 +/- 4.5), and 30% = 102.5 +/- 10.3 (53.6 +/- 5.0). Test-retest variability of peak to peak (and negative peak amplitude) between repeated trials was 0.10 (0.14), 0.14 (0.14), 0.17 (0.15), and 0.21 (0.20) at 5%, 10%, 20%, and 30% respectively. A relationship was found between the S-MUAP amplitude and force (r2 = 0.78, df = 90, F = 160, P < 0.001). Increase in average S-MUAP amplitude with force suggests that STA performed only at low levels of contraction may result in a biased sampling and small average S-MUAP amplitudes.

Action Potentials↗
PubMed1997

Supervised mutual-information based feature selection for motor unit action potential classification.

A new supervised mutual information-based feature selection method is presented. Using real motor unit action potential (MUAP) data from 10 EMG signals, the performances of 32 time-sample feature sets, feature subsets selected using first- and second-order mutual information and features obtained using linear discriminant analysis (LDA) and principal component analysis (PCA) were evaluated using a minimum Euclidean distance (MED) classifier. The evaluation showed that by using only 20 first-order features or only 15 second-order features mean error rates and error rate variations equivalent to using all 32 samples or LDA or PCA could be obtained. The computational cost of first-order feature selection was considerably less than LDA, PCA and second-order feature selection. The performance of first-order features was further evaluated using a more robust classifier. Unlike the MED classifier, the robust classifier only assigned a candidate MUAP if the assignment was sufficiently certain. For the robust classifier the average error rates using 20 features were similar to using the full feature set, yet higher assignment rates were obtained. Results from both evaluations suggest that the sets of first-order features were an efficient representation of lower dimension, which provided high accuracy classification with reduced computational requirements.

Action Potentials↗
PubMed1996

Resolving superimposed motor unit action potentials.

A new algorithm to resolve superimposed motor unit action potentials (MUAPs) is described, which uses a reduced search space and is based on the peel off approach. Knowledge specific to the problem domain, such as temporal relationships between and within motor unit action potential trains and MUAP energy information, is used to reduce the search space of motor units, possibly contributing to a superposition. The algorithm is tested using real electromyographic signals, and it demonstrates robust performance across the signals tested. For the signals tested, the average total resolution rate is 94%, the average correct resolution rate is 99.2% and the average error rate is 0.85%.

Action Potentials↗
PubMed1996

Adaptive motor unit action potential clustering using shape and temporal information.

An adaptive algorithm is described that groups motor unit action potentials (MUAPs), detected in a composite EMG signal during signal decomposition, and creates partial motor unit action potential trains (MUAPTs). Data-driven MUAP shape and motor unit firing-pattern based criteria are used to form the clusters. An algorithm for estimating MUAPT temporal parameters, which provides accurate estimates even for partially defined trains, is used to obtain firing-pattern information. No a priori knowledge is required regarding the number of clusters or the distribution of their template shapes. The clustering algorithm when applied to real concentric-needle detected MUAP data provides accurate and useful clustering results. Compared to a classical leader-based algorithm, it provides more robust performance, is better able to estimate the true number of motor units represented in a set of detected MUAPs, and obtains more complete and accurate MUAPTs.

Action Potentials↗
PubMed1996

Robust method for estimating motor unit firing-pattern statistics.

An error-filtered estimation (EFE) algorithm for estimating the mean and standard deviation of a set of time intervals between consecutive motor unit firing times (inter-pulse intervals (IPIs)) is described. As the input IPI data are filtered and only valid IPIs are used to estimate mean and standard deviation values, the EFE algorithm provides accurate estimates even when the data defining the train of motor unit firing times are only partially complete or have several erroneous firing times. The algorithm has been evaluated using both simulated and real motor unit firing time data, and has been found to provide accurate and unbiased mean and standard deviation estimates, even when up to 70% of the IPI data are incorrect.

Electromyography↗
PubMed1992

Detection of motor unit action potentials with surface electrodes: influence of electrode size and spacing.

A model of the motor unit action potential was developed to investigate the amplitude and frequency spectrum contributions of motor units, located at various depths within muscle, to the surface detected electromyographic (EMG) signal. A dipole representation of the transmembrane current in a three-dimensional muscle volume was used to estimate detected individual muscle fiber action potentials. The effects of anisotropic muscle conductance, innervation zone location, propagation velocity, fiber length, electrode area, and electrode configuration were included in the fiber action potential model. A motor unit action potential was assumed to be the sum of the individual muscle fiber action potentials. A computational procedure, based on the notion of isopotential layers, was developed which substantially reduced the calculation time required to estimate motor unit action potentials. The simulations indicated that: 1) only those motor units with muscle fibers located within 10-12 mm of the electrodes would contribute significant signal energy to the surface EMG, 2) variation in surface area of electrodes has little effect on the detection depth of motor unit action potentials, 3) increased interelectrode spacing moderately increases detection depth, and 4) the frequency content of action potentials decreases steeply with increased electrode-motor unit territory distance.

Action Potentials↗