High-yield decomposition of surface EMG signals

Abstract

Objective: Automatic decomposition of surface electromyographic (sEMG) signals into their constituent motor unit action potential trains (MUAPTs).

Methods: A small five-pin sensor provides four channels of sEMG signals that are in turn processed by an enhanced artificial intelligence algorithm evolved from a previous proof-of-principle. We tested the technology on sEMG signals from five muscles contracting isometrically at force levels ranging up to 100% of their maximal level, including those that were covered with more than 1.5cm of adipose tissue. Decomposition accuracy was measured by a new method wherein a signal is first decomposed and then reconstructed and the accuracy is measured by comparison. Results were confirmed by the more established two-source method.

Results: The number of MUAPTs decomposed varied among muscles and force levels and mostly ranged from 20 to 30, and occasionally up to 40. The accuracy of all the firings of the MUAPTs was on average 92.5%, at times reaching 97%.

Conclusions: Reported technology can reliably perform high-yield decomposition of sEMG signals for isometric contractions up to maximal force levels.

Significance: The small sensor size and the high yield and accuracy of the decomposition should render this technology useful for motor control studies and clinical investigations.

Copyright 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Figures

Figure 1

A) The five-pin surface EMG sensor attached above the First Dorsal Interosseous muscle in the hand. B) Top and bottom views of the sensor. The four pins on the corner of a square are spaced 3.6 mm apart.

Figure 2

Block diagram of the sEMG signal decomposition algorithm. The PD-IPUS stage creates, matches, and updates Motor Unit Action Potential (MUAP) templates. The PD-IGAT stage performs MUAPT discrimination at the output of a shape-matching procedure applied to the sEMG signal.

Figure 3

Dot plots of inter-firing intervals of 30 MUAPTs obtained by decomposing signal # 1 of Table 1. The Inter-pulse-interval is plotted vertically. The vertical limit on each dot height is 200 milliseconds. The sEMG signal was collected during a 50% MVC of the FDI muscle. The numbers left of the vertical axis indicate the number and recruitment order of the motor units. Right vertical axis indicates %MVC level of force profile superimposed on the dot plot.

Figure 4

The top trace represents one channel of the sEMG signal taken from sample #5 in Table 1. It was obtained from a 50% MVC of the First Dorsal Interosseous muscle. The second is an expanded segment (0.5 s) of the raw signal. The bar raster contains the firing times of 28 motor units.

Figure 5

Bar plot with force profile for signal # 13 in Table 1 obtained from a 100% MVC of the Vastus Lateralis muscle. The muscle was covered with 14 mm of adipose tissue. Note that the last detected motor unit was recruited at approximately 80% MVC. Also, motor unit 15 is re-recruited when the force rises up for a second time.

Figure 6

Plot of entire waveform of a single channel of signal #2 in Table 1 from 50% MVC of the FDI muscle. Plotted directly below is the residual signal from decomposing the top waveform into 24 MUAPTs.

Figure 7

Plots of 39 MUAP shapes estimated for a 0.5s interval of signal # 20 in Table obtained from a 75% MVC of the Biceps Brachii muscle.

Figure 8

Illustration of “reconstruct-and-test” procedure for assessing the accuracy of the decomposition algorithm. An actual sEMG signal s(n) is decomposed to identify its MUAPTs. Signal y(n) is synthesized by summing together the decomposed MUAPTs of s(n) and white Gaussian noise whose variance is set equal to that of the residual signal from the decomposition. The reconstituted y(n) signal is then decomposed and compared to the decomposed MUAPTs of s(n). The ellipses indicate places in the figure where the MUAPTs of y(n) are different from the corresponding MUAPTs of s(n).

Figure 9

A) Histogram plot indicating the accuracies (expressed as percentages) of the decomposition for the 561 MUAPTs of the entire set of sEMG signals listed in Table 1. The mean ± standard deviation of the accuracies is 92.5±3.7 percent. B) Bar graph of the MUAPT accuracy distribution for each of the 22 signals listed in Table 1. The non-shaded portion of each signal’s bar indicates the percentage of its MUAPTs that have accuracy above 90%. The gray portion of each bar indicates the percentage of a signal’s MUAPTs that have accuracies between 85% and 90%. The darkest portion of each bar represents the percentage of a signal’s MUAPTs with accuracies below 85%.

Figure 10

A) A comparison of all the firings of 11 MUAPTs that were identified by the decomposition of two signal sets obtained from two sensors located on the First Dorsal Interosseous muscle contracting at 50% MVC. The first signal set decomposed into 30 MUAPTs and the second into 31 MUAPTs. Eleven (11) were common in both signals sets. The blue bars correspond to the MUAPTs from sensor #1 while the black bars correspond to those of sensor #2. B) A magnified interval of 2s from the two bar plots illustrates the simultaneous occurrence of the firings from an individual motor unit seen in each of the two sensors.

Figure 11

A) An illustration of changing MUAP shapes of a motor unit throughout a contraction. The three shapes represent the MUAP of the same motor unit as it appears at different times (1, 11, and 21 s) in the steady region of a contraction. B) An illustration of the similarity of the shapes of different MUAPs occurring during an epoch of a contraction.

Figure 12

Top Plot: A single channel of a filtered 12 ms segment of the sEMG signal obtained from the First Dorsal Interosseous muscle contracting at 50% MVC. Bottom Plots: Nine Motor unit action potentials found by the sEMG decomposition algorithm to be in superposition in the 20 millisecond segment. The sum of all 9 Motor unit action potentials yields the trace in the top plot. This figure shows the capacity of the decomposition algorithm to extract action potentials from a complex superposition.

Figure 13

Polyphasic action potentials derived from the decomposition of surface EMG signals. Both are from elderly subjects, 79 and 78 years old. Both samples were obtained from the First Dorsal Interosseous (FDI) muscle. The polyphasic action potential in the top trace was from a motor unit recruited at 10% MVC and that in the bottom trace from a motor unit recruited at 45% MVC.