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Biomedical subjects

Madeleine M Lowery

Publications and source records attributed to Madeleine M Lowery.

12 recordsLinked to original sources
PubMed2006

Simulation of intramuscular EMG signals detected using implantable myoelectric sensors (IMES).

The purpose of this study was to test the feasibility of recording independent electromyographic (EMG) signals from the forearm using implantable myoelectric sensors (IMES), for myoelectric prosthetic control. Action potentials were simulated using two different volume conductor models: a finite-element (FE) model that was used to explore the influence of the electrical properties of the surrounding inhomogeneous tissues and an analytical infinite volume conductor model that was used to estimate the approximate detection volume of the implanted sensors. Action potential amplitude increased progressively as conducting electrodes, the ceramic electrode casing and high resistivity encapsulation tissue were added to the model. For the muscle fiber locations examined, the mean increase in EMG root mean square amplitude when the full range of material properties was included in the model was 18.2% (+/-8.1%). Changing the orientation of the electrode with respect to the fiber direction altered the shape of the electrode detection volume and reduced the electrode selectivity. The estimated detection radius of the IMES electrode, assuming a cylindrical muscle cross section, was 4.8, 6.2, and 7.5 mm for electrode orientations of 0 degree, 22.5 degrees, and 45 degrees with respect to the muscle fiber direction.

Action Potentials↗
PubMed2005

A simulation study to examine the effect of common motoneuron inputs on correlated patterns of motor unit discharge.

The influence of common oscillatory inputs to the motoneuron pool on correlated patterns of motor unit discharge was examined using model simulations. Motor unit synchronization, in-phase fluctuations in mean firing rates known as 'common drive', and the coefficient of variation of the muscle force were examined as the frequency and amplitude of common oscillatory inputs to the motoneuron pool were varied. The amount of synchronization, the peak correlation between mean firing rates and the coefficient of variation of the force varied with both the frequency and amplitude of the common input signal. Values for 'common drive' and the force coefficient of variation were highest for oscillatory inputs at frequencies less than 5 Hz, while synchronization reached a maximum when the frequency of the common input was close to the average motor unit firing rate. The frequency of the common input signal for which the highest levels of synchronization were observed increased as motoneuron firing rates increased in response to higher target force levels. The simulation results suggest that common low-frequency oscillations in motor unit firing rates and short-term synchronization result from oscillatory activity in different bands of the frequency spectrum of shared motoneuron inputs. The results also indicate that the amount of synchronization between motor unit discharges depends not only on the amplitude of the shared input signal, but also on its frequency in relation to the present firing rates of the individual motor units.

Action Potentials↗
PubMed2005

A technique to track individual motor unit action potentials in surface EMG by monitoring their conduction velocities and amplitudes.

The speed of propagation of an action potential along a muscle fiber, its conduction velocity (CV), can be used as an indication of the physiological or pathological state of the muscle fiber membrane. The motor unit action potential (MUAP), the waveform resulting from the spatial and temporal summation of the individual muscle fiber action potentials of that motor unit (MU), propagates with a speed referred to as the motor unit conduction velocity (MUCV). This paper introduces a new algorithm, the MU tracking algorithm, which estimates MUCVs and MUAP amplitudes for individual MUs in a localized MU population using SEMG signals. By tracking these values across time, the electrical activity of the localized MU pool can be monitored. An assessment of the performance of the algorithm has been achieved using simulated SEMG signals. It is concluded that this analysis technique enhances the suitability of SEMG for clinical applications and points toward a future of noninvasive diagnosis and assessment of neuromuscular disorders.

Action Potentials↗
PubMed2004

Time and frequency domain methods for quantifying common modulation of motor unit firing patterns.

BACKGROUND: In investigations of the human motor system, two approaches are generally employed toward the identification of common modulating drives from motor unit recordings. One is a frequency domain method and uses the coherence function to determine the degree of linear correlation between each frequency component of the signals. The other is a time domain method that has been developed to determine the strength of low frequency common modulations between motor unit spike trains, often referred to in the literature as 'common drive'. METHODS: The relationships between these methods are systematically explored using both mathematical and experimental procedures. A mathematical derivation is presented that shows the theoretical relationship between both time and frequency domain techniques. Multiple recordings from concurrent activities of pairs of motor units are studied and linear regressions are performed between time and frequency domain estimates (for different time domain window sizes) to assess their equivalence. RESULTS: Analytically, it may be demonstrated that under the theoretical condition of a narrowband point frequency, the two relations are equivalent. However practical situations deviate from this ideal condition. The correlation between the two techniques varies with time domain moving average window length and for window lengths of 200 ms, 400 ms and 800 ms, the r2 regression statistics (p < 0.05) are 0.56, 0.81 and 0.80 respectively. CONCLUSIONS: Although theoretically equivalent and experimentally well correlated there are a number of minor discrepancies between the two techniques that are explored. The time domain technique is preferred for short data segments and is better able to quantify the strength of a broad band drive into a single index. The frequency domain measures are more encompassing, providing a complete description of all oscillatory inputs and are better suited to quantifying narrow ranges of descending input into a single index. In general the physiological question at hand should dictate which technique is best suited.

Journal Article↗
PubMed2004

Volume conduction in an anatomically based surface EMG model.

A finite-element model to simulate surface electromyography (EMG) in a realistic human upper arm is presented. The model is used to explore the effect of limb geometry on surface-detected muscle fiber action potentials. The model was based on magnetic resonance images of the subject's upper arm and includes both resistive and capacitive material properties. To validate the model geometry, experimental and simulated potentials were compared at different electrode sites during the application of a subthreshold sinusoidal current source to the skin surface. Of the material properties examined, the closest approximation to the experimental data yielded a mean root-mean-square (rms) error of the normalized surface potential of 18% or 27%, depending on the site of the applied source. Surface-detected action potentials simulated using the realistic volume conductor model and an idealized cylindrical model based on the same limb geometry were then compared. Variation in the simulated limb geometry had a considerable effect on action potential shape. However, the rate of decay of the action potential amplitude with increasing distance from the fiber was similar in both models. Inclusion of capacitive material properties resulted in temporal low-pass filtering of the surface action potentials. This effect was most pronounced in the end-effect components of action potentials detected at locations far from the active fiber. It is concluded that accurate modeling of the limb geometry, asymmetry, tissue capacitance and fiber curvature is important when the specific action potential shapes are of interest. However, if the objective is to examine more qualitative features of the surface EMG signal, then an idealized volume conductor model with appropriate tissue thicknesses provides a close approximation.

Action Potentials↗
PubMed2003

Independence of myoelectric control signals examined using a surface EMG model.

The detection volume of the surface electromyographic (EMG) signal was explored using a finite-element model, to examine the feasibility of obtaining independent myoelectric control signals from regions of reinnervated muscle. The selectivity of the surface EMG signal was observed to decrease with increasing subcutaneous fat thickness. The results confirm that reducing the interelectrode distance or using double-differential electrodes can increase surface EMG selectivity in an inhomogeneous volume conductor. More focal control signals can be obtained, at the expense of increased variability, by using the mean square value, rather than the root mean square or average rectified value.

Action Potentials↗
PubMed2003

Analysis and simulation of changes in EMG amplitude during high-level fatiguing contractions.

Changes in surface electromyographic (EMG) amplitude during sustained, fatiguing contractions are commonly attributed to variations in muscle fiber conduction velocity (MFCV), motor unit firing rates, transmembrane action potentials and the synchronization or recruitment of motor units. However, the relative contribution of each factor remains unclear. Analytical relationships relating changes in MFCV and mean motor unit firing rates to the root mean square (RMS) and average rectified (AR) value of the surface EMG signal are derived. The relationships are then confirmed using model simulation. The simulations and analysis illustrate the different behaviors of the surface EMG RMS and AR value with changing MFCV and firing rate, as the level of motor unit superposition varies. Levels of firing rate modulation and short-term synchronization that, combined with variations in MFCV, could cause changes in EMG amplitude similar to those observed during sustained isometric contraction of the brachioradialis at 80% of maximum voluntary contraction were estimated. While it is not possible to draw conclusions about changes in neural control without further information about the underlying motor unit activation patterns, the examples presented illustrate how a combined analytical and simulation approach may provide insight into the manner in which different factors affect EMG amplitude during sustained isometric contractions.

Adult↗
PubMed2003

Finite-element time-domain algorithms for modeling linear Debye and Lorentz dielectric dispersions at low frequencies.

We present what we believe to be the first algorithms that use a simple scalar-potential formulation to model linear Debye and Lorentz dielectric dispersions at low frequencies in the context of finite-element time-domain (FETD) numerical solutions of electric potential. The new algorithms, which permit treatment of multiple-pole dielectric relaxations, are based on the auxiliary differential equation method and are unconditionally stable. We validate the algorithms by comparison with the results of a previously reported method based on the Fourier transform. The new algorithms should be useful in calculating the transient response of biological materials subject to impulsive excitation. Potential applications include FETD modeling of electromyography, functional electrical stimulation, defibrillation, and effects of lightning and impulsive electric shock.

Algorithms↗
PubMed2002

A simulation study to examine the use of cross-correlation as an estimate of surface EMG cross talk.

Cross-correlation between surface electromyogram (EMG) signals is commonly used as a means of quantifying EMG cross talk during voluntary activation. To examine the reliability of this method, the relationship between cross talk and the cross-correlation between surface EMG signals was examined by using model simulation. The simulation results illustrate an increase in cross talk with increasing subcutaneous fat thickness. The results also indicate that the cross-correlation function decays more rapidly with increasing distance from the active fibers than cross talk, which was defined as the normalized EMG amplitude during activation of a single muscle. The influence of common drive and short-term motor unit synchronization on the cross-correlation between surface EMG signals was also examined. While common drive did not alter the maximum value of the cross-correlation function, the correlation increased with increasing motor unit synchronization. It is concluded that cross-correlation analysis is not a suitable means of quantifying cross talk or of distinguishing between cross talk and coactivation during voluntary contraction. Furthermore, it is possible that a high correlation between surface EMG signals may reflect an association between motor unit firing times, for example due to motor unit synchronization.

Adipose Tissue↗
PubMed2002

A multiple-layer finite-element model of the surface EMG signal.

The effect of skin, muscle, fat, and bone tissue on simulated surface electromyographic (EMG) signals was examined using a finite-element model. The amplitude and frequency content of the surface potential were observed to increase when the outer layer of a homogeneous muscle model was replaced with highly resistive skin or fat tissue. The rate at which the surface potential decreased as the fiber was moved deeper within the muscle also increased. Similarly, the rate at which the surface potential decayed around the surface of the model, for a constant fiber depth, increased. When layers of subcutaneous fat of increasing thickness were then added to the model, EMG amplitude, frequency content, and the rate of decay of the surface EMG signal around the limb decreased, due to the increased distance between the electrodes and the active fiber. The influence of bone on the surface potential was observed to vary considerably, depending on its location. When located close to the surface of the volume conductor, the surface EMG signal between the bone and the source and directly over the bone increased, accompanied by a slight decrease on the side of the bone distal to the active fiber. The results emphasize the importance of distinguishing between the effects of material properties and the distance between source and electrode when considering the influence of subcutaneous tissue, and suggest possible distortions in the surface EMG signal in regions where a bone is located close to the skin surface.

Action Potentials↗
PubMed2002

Frequency- and time-domain FEM models of EMG: capacitive effects and aspects of dispersion.

Electromyography (EMG) simulations have traditionally been based on purely resistive models, in which capacitive effects are assumed to be negligible. Recent experimental studies suggest these assumptions may not be valid for muscle tissue. Furthermore, both muscle conductivity and permittivity are frequency-dependent (dispersive). In this paper, frequency-domain and time-domain finite-element models are used to examine the impact of capacitive effects and dispersion on the surface potential of a volume conductor. The results indicate that the effect of muscle capacitance and dispersion varies dramatically. Choosing low conductivity and high permittivity values in the range of experimentally reported data for muscle can cause displacement currents that are larger than conduction currents with corresponding reduction in surface potential of up to 50% at 100 Hz. Conductivity and permittivity values lying toward the middle of the reported range yield results which do not differ notably from purely resistive models. Also, excluding dispersion can also cause large error-up to 75% in the high frequency range of the EMG. It is clear that there is a need to establish accurate values of both conductivity and permittivity for human muscle tissue in vivo in order to quantify the influence of capacitance and dispersion on the EMG signal.

Computer Simulation↗