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Previous observations suggest that elevated high-pass filter settings (1,600-3,200 HZ) can reveal greater motor unit action potential (MUAP) complexity (turns). We assessed the effect of high-pass filter settings (500, 1,000, 2,000 HZ) on MUAP metrics. MUAPs were recorded with a concentric needle and initially extracted by a decomposition software algorithm at 10 HZ-10 kHZ and further filtered offline at 500, 1,000, and 2,000 HZ. When reanalyzed by the decomposition software there were marked reductions in peak-peak amplitude, area, area-to-amplitude ratio, and duration at the 500 HZ filter with lesser subsequent reductions at higher filter settings. In contrast, turn and phase counts did not change significantly. Individual MUAPs tracked across filter settings showed rare increases in turn count at the 500 HZ setting but a subsequent decrease in counts with higher filter settings. We conclude that the routine use of elevated high-pass filters, as in quantitative EMG analysis, does not enhance turn count.

Motor unit number estimation (MUNE) is a unique electrophysiologic test used to estimate the number of surviving motor units in a muscle or group of muscles. It is used most frequently to monitor lower motor neuron loss in amyotrophic lateral sclerosis and spinal muscle atrophy. Of particular interest is its use as an endpoint measure in clinical trials for these diseases. This article describes the principles of MUNE and the factors that need to be considered, and reviews several techniques that have been used in clinical trials and in monitoring progression. It then reviews experience with MUNE in clinical trials for amyotrophic lateral sclerosis and spinal muscle atrophy and discusses how MUNE correlates with measures of function.

Quantitative electromyography (QEMG) relies on a number of discharges of the same motor unit action potential (MUAP) from a train to create an averaged MUAP considered to be representative of a true potential. The train of potentials may be affected by changes in position of the electrode relative to contributing muscle fibers of the motor unit due to operator or subject movement. The effect of changes in electrode position, along with consideration for patient comfort, prompted this study to determine the shortest duration of recording time necessary for sufficient data acquisition for QEMG studies. We determined that 10 seconds of moderate muscle activity is the most reasonable acquisition duration to isolate up to 6 MUAPs at a given electrode site and minimize the effects of movement artifact in the signal.

The authors discuss the techniques and use of electrodiagnosis to help fully characterize peripheral neuropathies, including electrodiagnostic principles, normal findings, correlations between underlying nerve pathology that leads to abnormal electrodiagnostic findings, and how to detect and interpret electrodiagnostic findings.

Quantitative EMG (QEMG) is a useful technique to diagnose subtle neuromuscular disorders. Automated QEMG techniques rely upon computer algorithms to extract motor unit action potentials (MUAPs). A number of different algorithms are available and we have compared the performance of three of them. We have also assessed a number of variables related to the operations performed by the algorithms, including marking of MUAP metrics, data acquisitions time, and effects of raising the high-pass filter. We have also assessed whether there are differences in MUAP metrics depending upon where the needle is placed in the muscle.

Previous observations suggest that elevated high-pass filter settings (1,600-3,200 HZ) can reveal greater motor unit action potential (MUAP) complexity (turns). We assessed the effect of high-pass filter settings (500, 1,000, 2,000 HZ) on MUAP metrics. MUAPs were recorded with a concentric needle and initially extracted by a decomposition software algorithm at 10 HZ-10 kHZ and further filtered offline at 500, 1,000, and 2,000 HZ. When reanalyzed by the decomposition software there were marked reductions in peak-peak amplitude, area, area-to-amplitude ratio, and duration at the 500 HZ filter with lesser subsequent reductions at higher filter settings. In contrast, turn and phase counts did not change significantly. Individual MUAPs tracked across filter settings showed rare increases in turn count at the 500 HZ setting but a subsequent decrease in counts with higher filter settings. We conclude that the routine use of elevated high-pass filters, as in quantitative EMG analysis, does not enhance turn count.

Quantitative electromyography (QEMG) relies on a number of discharges of the same motor unit action potential (MUAP) from a train to create an averaged MUAP considered to be representative of a true potential. The train of potentials may be affected by changes in position of the electrode relative to contributing muscle fibers of the motor unit due to operator or subject movement. The effect of changes in electrode position, along with consideration for patient comfort, prompted this study to determine the shortest duration of recording time necessary for sufficient data acquisition for QEMG studies. We determined that 10 seconds of moderate muscle activity is the most reasonable acquisition duration to isolate up to 6 MUAPs at a given electrode site and minimize the effects of movement artifact in the signal.

Publisher Summary This chapter reviews that routine electromyography (EMG) relies on qualitative assessment and judgment of waveform patterns, and when made by experienced electromyographers can be accurate and insightful in recognizing the normal from the abnormal, and separating neuropathic from myopathic motor units. It discusses that automated quantitative electromyography (QEMG) techniques rely upon computer algorithms to extract motor unit action potentials (MUAPs). QEMG is not widely used in routine clinical practice due to increased operational complexity and time required to perform a study, plus a lack of uniformity in its operational use. A number of different algorithms are available and compares the performance of three of them. The chapter assesses a number of variables related to the operations performed by the algorithms including: marking of MUAP metrics, data acquisitions time, and effects of raising the high-pass filter. It also assesses whether there are differences in MUAP metrics depending upon where the needle is placed in the muscle.

It is unclear whether there are clinically significant differences in amplitude, duration, and numbers of turns and phases if an electromyographic (EMG) study is performed near to, or far from, the end-plate zone. The effects of temporal dispersion of arriving muscle-fiber action potentials on quantitative motor unit action potential (MUAP) metrics were assessed in simulated and biologic muscles. Two muscle simulation models were studied with electrode recording positions near the motor end-plate zone and 50-75 mm away. When the electrode was moved away from the end-plate zone, averages of 20 MUAPs significantly decreased in amplitude and area, and increased in numbers of turns and phases, but there was no significant change in duration. In biologic muscles (both normal and pathologic), similar changes in average metrics were observed, but to lesser degrees; few were statistically significant. Zones of innervation in biologic muscles are broadly distributed and, during routine electrode studies, distances between random electrode placements and end-plate zones are therefore relatively short, leading to clinically insignificant changes in quantitative MUAP metrics with distance from the end-plate zone. Thus, electrode position within a muscle is unlikely to affect clinical MUAP interpretation.

Quantitative EMG (QEMG) techniques include automated motor unit action potential (MUAP) detection and marking of clinically useful waveform metrics. Different computer algorithms are available on modern EMG machines to perform these operations rapidly. However, the efficiency and accuracy of available algorithms are rarely directly compared. We have assessed three commercially available algorithms using both synthesized and biologic interference patterns and found differences among algorithms, some of which are clinically significant. Our results point out the importance of assessing for duplicate MUAPs (same waveform detected as two separate waveforms) and accuracy of markings used to determine MUAP metrics.