Daniel W. Stashuk

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.

Motor unit number estimates in the tibialis anterior muscle of young, old, and very old men

The rate of motor unit (MU) loss and its influence on the progression of sarcopenia is not well understood. Therefore, the main purpose of this study was to estimate and compare numbers of MUs in the tibialis anterior (TA) of young men (∼25 years) and two groups of older men (∼65 years and ≥80 years). Decomposition‐enhanced spike‐triggered averaging was used to collect surface and intramuscular electromyographic signals during isometric dorsiflexions at 25% of maximum voluntary contraction. The mean surface‐MU potential size was divided into the maximum M wave to calculate the motor unit number estimate (MUNE). The MUNE was significantly reduced in the old (91) compared to young (150) men, and further reduced in the very old men (59). Despite the smaller MUNE at age 65, strength was not reduced until beyond 80 years. This suggests that age‐related MU loss in the TA does not limit function until a critical threshold is reached. Muscle Nerve, 2005

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.

Decomposition-based quantitative electromyography: Methods and initial normative data in five muscles

Quantitative electromyographic (EMG) techniques provide clinically useful information to aid in the diagnosis and follow the course or response to treatment of diseases affecting the motor system. The purpose of this study was to describe a decomposition‐based quantitative electromyography method (DQEMG) designed to obtain clinically applicable information relating to motor unit potential (MUP) size and configuration, and motor unit (MU) firing characteristics. Additionally, preliminary normative data were obtained from the deltoid, biceps brachii, first dorsal interosseous, vastus medialis, and tibialis anterior muscles of 13 control subjects. DQEMG was capable of efficiently and accurately extracting MUP data from complex interference patterns during mild to moderate contractions. MUP amplitude, surface‐detected MUP (S‐MUP) amplitude, MUP duration, number of phases, and MU firing frequencies varied significantly across muscles. The mean parameter values for the individual muscles studied were similar to previous reports based on other quantitative methods. The main advantages of this method are the speed of data acquisition and processing, the ability to obtain MUPs from MUs with low and higher recruitment thresholds, and the ability to obtain both S‐MUP or macro‐MUP data as well as MU firing rate information. Muscle Nerve 28: 204–211, 2003

Motor unit number estimation by decomposition-enhanced spike-triggered averaging: control data, test-retest reliability, and contractile level effects

Decomposition‐enhanced spike‐triggered averaging (DE‐STA) has been developed as a method for obtaining a motor unit number estimate (MUNE). We describe the method and report control data for the first dorsal interosseous/adductor pollicis and thenar muscles and reliability in the thenar muscles. Seventeen subjects (ages 20–50 years) took part in the study. The maximum M potential was elicited with supramaximal stimulation of the ulnar or median nerve at the wrist. Surface and intramuscularly detected electromyographic signals were then collected simultaneously during mild to moderate contractions. Decomposition algorithms were used to detect and sort the individual motor unit potential (MUP) occurrences of several concurrently active motor units in the needle‐detected signals. The MUP occurrences were used as triggering sources to estimate their corresponding surface‐detected MUPs (S‐MUPs) using STA. The mean S‐MUP size was calculated and divided into the maximum M‐potential size to derive a MUNE. The MUNE values were consistent with those previously reported with other methods, and thenar MUNEs for the two trials were similar (249 ± 78 and 246 ± 90), with high test–retest reliability (r = 0.94, P < 0.05). DE‐STA thus appears to be a valid and reliable method to obtain MUNEs. Muscle Nerve 29: 693–699, 2004

Physiologically based simulation of clinical EMG signals

An algorithm that generates electromyographic (EMG) signals consistent with those acquired in a clinical setting is described. Signals are generated using a model constructed to closely resemble the physiology and morphology of skeletal muscle, combined with line source models of commonly used needle electrodes positioned in a way consistent with clinical studies. The validity of the simulation routines is demonstrated by comparing values of statistics calculated from simulated signals with those from clinical EMG studies of normal subjects. The simulated EMG signals may be used to explore the relationships between muscle structure and activation and clinically acquired EMG signals. The effects of motor unit (MU) morphology, activation, and neuromuscular junction activity on acquired signals can be analyzed at the fiber, MU and muscle level. Relationships between quantitative features of EMG signals and muscle structure and activation are discussed.

Decomposition-based quantitative electromyography: effect of force on motor unit potentials and motor unit number estimates

Decomposition‐based quantitative electromyography (DQEMG) allows for the collection of motor unit potentials (MUPs) over a broad range of force levels. Given the size principle of motor unit recruitment, it may be necessary to control for force when using DQEMG for the purpose of deriving a motor unit number estimate (MUNE). Therefore, this study was performed to examine the effect of force on the physiological characteristics of concentric needle‐ and surface‐detected MUPs and the subsequent impact on MUNEs obtained from the first dorsal interosseous (FDI) muscle sampled using DQEMG. Maximum M waves were elicited in 10 subjects with supramaximal stimulation of the ulnar nerve at the wrist. Intramuscular and surface‐detected EMG signals were collected simultaneously during 30‐s voluntary isometric contractions performed at specific percentages of maximal voluntary contraction (MVC). Decomposition algorithms were used to identify needle‐detected MUPs and their individual MU firing times. These MU firing times were used as triggers to extract their corresponding surface‐detected MUPs (S‐MUPs) using spike‐triggered averaging. A mean S‐MUP was then calculated, the size of which was divided into the maximum M‐wave size to derive a MUNE. Increased levels of contraction had a significant effect on needle‐ and surface‐detected MUP size, firing rate, and MUNE. These results suggest that force level is an important factor to consider when performing quantitative EMG, including MUNEs with this method. Muscle Nerve, 2005

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.

Neurogenic Changes in the Upper Airway of Patients with Obstructive Sleep Apnea

RATIONALE Controversy persists regarding the presence and importance of hypoglossal nerve dysfunction in obstructive sleep apnea (OSA). OBJECTIVES We assessed quantitative parameters related to motor unit potential (MUP) morphology derived from electromyographic (EMG) signals in patients with OSA versus control subjects and hypothesized that signs of neurogenic remodeling would be present in the patients with OSA. METHODS Participants underwent diagnostic sleep studies to obtain apnea-hypopnea indices. Muscle activity was detected with 50-mm concentric needle electrodes. The concentric needle was positioned at more than 10 independent sites per subject, after the local anatomy of the upper airway musculature was examined by ultrasonography. All activity was quantified with subjects awake, during supine eupneic breathing while wearing a nasal mask connected to a pneumotachograph. Genioglossus EMG signals were analyzed offline by automated software (DQEMG), which extracted motor unit potential trains (MUPTs) contributed by individual motor units from the composite EMG signals. Quantitative measurements of MUP templates, including duration, peak-to-peak amplitude, area, area-to-amplitude ratio, and size index, were compared between the untreated patients with OSA and healthy control subjects. MEASUREMENTS AND MAIN RESULTS A total of 1,655 MUPTs from patients with OSA (n = 17; AHI, 55 ± 6/h) and control subjects (n = 14; AHI, 4 ± 1/h) were extracted from the genioglossus muscle EMG signals. MUP peak-to-peak amplitudes in the patients with OSA were not different compared with the control subjects (397.5 ± 9.0 vs. 382.5 ± 10.0 μV). However, the MUPs of the patients with OSA were longer in duration (11.5 ± 0.1 vs. 10.3 ± 0.1 ms; P < 0.001) and had a larger size index (4.09 ± 0.02 vs. 3.92 ± 0.02; P < 0.001) compared with control subjects. CONCLUSIONS These results confirm and quantify the extent and existence of structural neural remodeling in OSA.

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.