Biomedical subjects

D W Stashuk

Publications and source records attributed to D W Stashuk.

8 recordsLinked to original sources

PubMed1999

Detecting single fiber contributions to motor unit action potentials.

The ability to detect muscle fiber action potential (MFAP) contributions to motor unit action potentials (MUAPs) measured using single fiber (SF) and concentric needle (CN) electrodes was studied using simulated MUAPs. Various MFAP-acceleration thresholds were used to define significant fiber contributions. Attempts to detect the significant MFAP contributions, by locating peaks in filtered MUAPs or MUAP accelerations using various MUAP-based thresholds, were then made. Considering filtered MUAPs and a significant contribution threshold of 7.5 kV/s2, and using fiber-density peak-detection criteria, at best 46% and 50% of significant MFAP contributions were detected for the SF and CN MUAPs, respectively. Considering MUAP accelerations and a significant contribution threshold of 7.5 kV/s2, 80% and 84% of significant MFAP contributions could be detected, respectively. Most significant contributions were created from fibers located within approximately 350 microm of the electrode. The results suggest that significant peaks, defined using MUAP-based thresholds, within the acceleration of CN MUAPs can strongly correspond to individual fiber activity and may be useful for measuring fiber density and neuromuscular jitter.

Action Potentials↗

PubMed1998

A longitudinal study of the pathophysiological changes in single human thenar motor units in amyotrophic lateral sclerosis.

The sequence of pathophysiological changes in amyotrophic lateral sclerosis (ALS) at the single motor unit (MU) level is not well understood. Using a recently described technique, a comprehensive range of physiological properties in two thenar MUs in ALS were intensively studied. In the first MU, despite a marked decline in the ability of the subject to voluntarily recruit the MU, the physiological properties of this MU remained remarkably stable over a 2-year period. In contrast, the physiological properties of the other MU declined rapidly over 5 months despite the fact that this MU could be recruited with ease throughout the study period. These differences between the progressively dysfunctional changes in these two MUs illustrates the value of such longitudinal studies of specific MUs in improving our understanding of the evolution of changes in single motoneurons in ALS. The broader application of longitudinally tracking the pathophysiological changes of the surviving MUs may prove to be a sensitive measure of disease progression and in evaluating the effectiveness of treatments.

Action Potentials↗

PubMed1995

Methods for estimating the numbers of motor units in human muscles.

After the introduction by A. J. McComas of the original method for estimating the number of motor units based on manual incremental stimulation of a motor nerve, several new techniques have been developed, designed to correct for some of the errors inherent in the original technique. These methods incorporate algorithms to adjust for alternation and, to a greater or lesser extent, automate the methods, rendering the techniques less subject to operator bias and various physiological and technical errors. This review explores the advantages and drawbacks in the multiple-point stimulation (MPS), spike-triggered averaging (STA), and decomposition-enhanced STA techniques, illustrates some of the current applications of the techniques, and explores some tantalizing prospects for new studies of motor-unit physiology in the future.

Algorithms↗

PubMed1994

Physiological properties of single thenar motor units in the F-response of younger and older adults.

The purpose of this study was to characterize the properties of single thenar motor units in the F-response of healthy younger (n = 15; age 33 +/- 11 years) and older subjects (n = 15; age 68 +/- 3 years). Trains of 300 stimuli at intensities evoking M-potentials 10%, 20%, and 30% of the peak-to-peak amplitude of the maximum M-potential, were delivered to the median nerve. In the young, observed firing probabilities of surface-detected motor unit action potentials (S-MUAPs) extracted from the F-response ranged from less than 1-10%, the S-MUAPs varied in size from 0.015% to 5.3% of the maximum M-potential negative peak area, and they were similar in size to the population of S-MUAPs collected by multiple point stimulation of the median nerve. The percentage difference between the slowest and fastest conducting fibers for individual subjects ranged from 8% to 20%, which translated to conduction velocities (CVs) of 48-68 m.s-1 (mean 59 +/- 4). The preceding were all independent of stimulus intensity. The S-MUAP sizes were significantly larger in older subjects (39%), and the range and distribution of motor unit CVs (38-61 m.s-1; mean 52 +/- 3) were markedly shifted to reflect a slower population of motor fibers. These findings suggest that age-related axonal slowing may uniformly affect all median motor fibers.

Action Potentials↗

PubMed1994

Motor unit number estimates based on the automated analysis of F-responses.

An automated technique for estimating the number of motor units based on single motor unit action potentials in the F-response is described. The average surface detected motor unit action potential (S-MUAP) was calculated from the datapoint-by-datapoint average of a sample of S-MUAPs automatically selected from a population of F-responses. The technique was applied to the thenar muscles of young (n = 18, aged 31 +/- 11 years) and older (n = 15, aged 68 +/- 3) subjects. Motor unit number estimates based on the automated selection of S-MUAPs from the F-responses compared well with those derived using a computer-assisted manual method for selecting S-MUAPs from the F-response (automated 245 +/- 105 vs. manual 241 +/- 100, r = 0.93) and were similar to estimates obtained using multiple point stimulation (219 +/- 77). The advantages of the automated technique for collecting S-MUAPs from the F-response include the ready tolerance of the technique by subjects, the minimal amount of operator interaction required, and the additional information relating to the conduction velocities and latencies of single motor axons.

Action Potentials↗

PubMed1993

Determinants of mean motor unit size: impact on estimates of motor unit number.

The purpose of this study was to compare two fundamentally different methods of deriving the average surface-detected motor unit action potential (S-MUAP) size from which to calculate a motor unit number estimate (MUNE), namely: (1) the simple arithmetic average of S-MUAP parameter values; and (2) a computer-derived datapoint by datapoint average waveform which takes account of differences in S-MUAP shapes and durations. Multiple point stimulation was used to collect representative samples of between 11 and 20 S-MUAPs (mean 15 +/- 2 SD) from the median-innervated thenar muscles of 20 healthy control subjects between 20 and 76 years of age (mean 48 +/- 19 SD). The average S-MUAP size based on peak-to-peak amplitude, negative peak amplitude, and negative peak area measurements was calculated using the two different methods. The mean S-MUAP sizes based on the average waveform were significantly lower in all cases than those based on the simple average of S-MUAP parameter values. Differences tended to be greatest for MUNEs based on peak-to-peak amplitude (35%), less for negative peak amplitude (20%), and least for negative peak area (16%).

Action Potentials↗

PubMed1992

Probabilistic inference-based classification applied to myoelectric signal decomposition.

A new probabilistic inference based technique (IBC) for the classification of motor unit action potentials (MUAP's) is presented. This new technique discovers statistically significant relationships in the data and uses these relationships to generate classification rules. The technique was applied to the classification of MUAP's extracted from simulated myoelectric signals. Its performance was compared to that of classical template matching algorithms (TBC) applied to the same data. Using 32 time samples as features to represent the MUAP's it was found that the IBC based technique performed significantly better (p less than 0.005) than the TBC algorithms (83.0 +/- 2.6% versus 78.1 +/- 2.8% peak correct classification performance). As the size of the training set was reduced or as increasing numbers of random classification errors were introduced into the training data, the performance of the IBC and TBC techniques declined similarly. IBC performance remained superior until very small training sets (less than 30 MUAP's per motor unit) or training sets with large numbers of errors (greater than 50%) were used. Because the probabilistic inference technique can utilize nominal data it has the potential to use declarative problem domain knowledge which conceivably could improve its performance.

Action Potentials↗

PubMedDate not supplied

Decomposition and quantitative analysis of clinical electromyographic signals.

Procedures for the quantitative analysis of clinical electromyographic (EMG) signals detected simultaneously using selective or micro and non-selective or macro electrodes are presented. The procedures first involve the decomposition of the micro signals and then the quantitative analysis of the resulting motor unit action potential trains (MUAPTs) in conjunction with the associated macro signal. The decomposition procedures consist of a series of algorithms that are successively and iteratively applied to resolve a composite micro EMG signal into its constituent MUAPTs. The algorithms involve the detection of motor unit action potentials (MUAPs), MUAP clustering and supervised classification and they use shape and firing pattern information along with data dependent assignment criteria to obtain robust performance across a variety of EMG signals. The accuracy, extent and speed with which a set of 10 representative 20-30 s, concentric needle detected, micro signals could be decomposed are reported and discussed. The decomposition algorithms had a maximum and average error rate of 2.5% and 0.7%, respectively, on average assigned 88.7% of the detected MUAPs and took between 4 to 8 s. Quantitative analysis techniques involving average micro and macro MUAP shapes, the variability of micro MUAPs shapes and motor unit firing patterns are described and results obtained from analysis of the data set used to evaluate the decomposition algorithms are summarized and discussed.

Action Potentials↗