Electromyographic (EMG) Decomposition. Tutorial. Hamid R. Marateb, PhD; Kevin C. McGill, PhD

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1 Electromyographic (EMG) Decomposition Tutorial Hamid R. Marateb, PhD; Kevin C. McGill, PhD H. Marateb is with the Biomedical Engineering Department, Faculty of Engineering, the University of Isfahan, Isfahan, Iran ). K. C. McGill is with the Veterans Affairs Palo Alto Health Care System, 3801 Miranda Ave., Palo Alto, CA 94304, USA ( Introduction This tutorial illustrates the basic steps in a simple single-channel iemg decomposition program based on the approach described in (1). It does not attempt to resolve superpositions, and so provides only an incomplete decomposition. It uses a robust method to estimate the firing statistics from the incomplete MUAP trains. The code for the program is provided as part of this tutorial. It runs in Matlab (The Mathworks, Inc) and requires the EMGlab program but no other Matlab toolboxes. It is assumed that the user has Matlab version 6.5 through 8.2 and has downloaded and installed the EMGlab program from To run the tutorial, first run EMGlab and load and filter the sample EMG file as described in the next section. Then run the tutorial script by typing "tutorial" into the Matlab command window. The script decomposes the signal and displays the intermediate results in a Matlab figure and in the command window. When the decomposition is finished, the script loads the final results into the EMGlab window, where they can be reviewed and edited. Figure A1. The initial segment of the iemg signal R dat, channel 1, after high-pass filtering at 1 khz.

2 iemg signal The signal used in this tutorial is R dat, which is supplied with the tutorial materials. This is a multichannel EMG signal recorded from the brachioradialis muscle during a steady low-force isometric contraction. The tutorial uses channel 1. This EMG signal was recorded with a monopolar electrode using wide analog filter settings, so that the MUAP waveforms are similar to those recorded in clinical EMG exams. In order to sharpen the MUAP spikes for decomposition, select the 1 khz filter setting in EMGlab. The first 100 ms of the filtered signal are shown in Fig. A1. Segmentation and alignment The first step in the decomposition procedure is to detect the spikes, or "active segments," in the signal. The program detects all the segments in which the signal amplitude exceeds 4 times the standard deviation of the background noise, using a fixed 2-ms window. Then it aligns the segments by their peaks using a high-resolution alignment algorithm (2). The results are shown in Fig. A2. Figure A2. The active segments and their two-dimensional scatterplot representations.

3 Before alignment, a number of recurring spike waveforms can be seen (Fig. A2a). However, the waveforms appear somewhat blurry because the individual occurrences are out of alignment by a fraction of a sampling interval. Aligning all the occurrences on the peak of their interpolated waveform makes them appear much sharper (Fig. A2c). To visualize the similarities and differences between the active segments more clearly, they are plotted as points in a twodimensional space in Figs. A2b and A2d. The coordinates of the points are the values of the first two singular value decomposition coefficients. Distinct clusters can be seen, and they are more sharply focused after alignment. Clustering The next step is to cluster the active segments to determine which ones are likely to correspond to valid MUAPs. This is done using a density-based clustering method called "Ordering Points to Identify the Clustering Structure" (OPTICS) (3,4). The OPTICS algorithm provides a simple one-dimensional visualization of the multi-dimensional data. It requires one parameter, MinPts, which specifies the minimum number of active segments that can be considered to constitute a valid cluster. In this tutorial, MinPts is set to 50, but a different value may be needed for other signals (4). The output of the OPTICS algorithm is shown in Fig. A3a. Each valley in the plot corresponds to a cluster of active segments that are more similar to each other than they are to any other active segments. The program identifies the valleys and chooses the lowest point in each valley to be cluster representative. In this case, the program identified four clusters, with the representatives shown in Fig. A3a-c. This means that in this signal, there are four distinct spikes that each occurred at least 50 times. These can be considered to correspond to valid MUs. In addition, there are two smaller clusters (C5 and C6) which were not recognized by OPTICS as valid MUs because they had fewer than 50 occurrences. There are also a number of individual or loosely clustered points that correspond to superpositions. Figure A3. (a) The output of the OPTICS clustering algorithm on the aligned active segments (MinPts=50). Four distinct valleys were identified. The lowest point in each valley was chosen as the cluster representative (circles). (b) The waveforms of the cluster representatives. (c) The cluster representatives in the two-dimensional scatterplot. Clusters C5 and C6 had fewer than 50 members and were not counted as valid MUs.

4 In this signal, the MUAPs of the four identified MUs are quite distinct and their clusters can be clearly separated. This is not necessarily the case for all signals. It is possible to have MUs whose MUAPs are very similar in shape, so that their clusters overlap. A practical decomposition program might take firing time information into account in the clustering procedure in order to deal with this problem (5). The small cluster C5 consists of very small spikes that just barely exceeded the detection threshold. These might correspond to a very small valid MU. MUAPs that just barely rise out of the background noise are often difficult to decompose with confidence, however, and we will ignore them in this tutorial. One of the active segments in the small cluster C6 is shown in Fig. A4. This is the same MUAP as that of cluster C1, except that it is registered on its positive peak rather than its negative peak. In this MUAP, the positive and negative peaks are about the same amplitude, and in the sampled signal sometimes the positive peak is greater and sometimes the negative peak. If cluster C6 had been recognized as a valid cluster, there would have been two clusters corresponding to MU 1. A practical decomposition program would have to check whether any cluster representatives are just time-shifted versions of each other (1). Figure A4. Representative active segments from clusters C1 and C6. They are time-shifted versions of the same spike, whose positive and negative peaks have about the same amplitude. MUAP classification In this step, each active segment is classified as either being an occurrence of one of the identified MUs or as being something else. The active segments are classified according to the cluster representatives they are closest to, but only if the distance is less than a certain threshold. The threshold value is different for each cluster and tries to estimate the distance that might be expected due to background noise and MUAP variability. The results are shown in Fig. A5. One problem that can be noticed is that several of the active segments in the small cluster C6 were classified as belonging to MU 3 even though they were really time-shifted versions of MU 1. A practical decomposition program might solve this problem by aligning the active segments with the cluster representatives before determining their distances.

5 Active segments that are not close to any of the cluster representatives are essentially classified as being something else other than an occurrence of one of the identified MUs. These segments could correspond to superpositions, noise bursts, or small, unidentified MUAPs. This simple classification procedure only identifies isolated occurrences of the MUs, not occurrences involved in superpositions. Therefore the result is an incomplete decomposition. Nevertheless, in provides enough information to accurately identify the MUAP waveforms and firing statistics. Annotation Figure A5. The results of classification. The tutorial script loads the fineal decomposition results into EMGlab where they can be reviewed and edited (Fig. A6). It also estimates the decomposability index, mean firing rate, and accuracy of each MU. The decomposability index is an indication of how distinguishable the MUAP is from the other MUAPs in the signal, given the overall complexity of the signal. A larger decomposability index means that isolated occurrences of the MUAP are likely to be clearly recognizable. Because the identified firing patterns may contain gaps and erroneous firings, a robust algorithm is used to estimate the mean firing rate. Based on this estimate and on the number of very short intervals in the firing pattern, which are probably erroneous, the script also estimates the accuracy of the decomposition, i.e., the percentage of discharges in each MUAP train that were identified correctly. As can be seen in Table 1, for this signal at least the mean firing rates and decomposition accuracies estimated from the incomplete firing patterns agreed quite well with the actual values. Accuracy assessment As the final step in this tutorial, the script assesses the accuracy of the decomposition by comparing it with the "gold standard" annotation in the file R G.eaf. This "gold standard" annotation was determined using EMGlab with manual checking the results. Due to the relative simplicity of this signal, it is possible to have a high degree of confidence that this "gold standard" annotation is full and correct.

6 Figure A6. The decomposition results shown in EMGlab. The regularity of the firing patterns is a indication of MU validity, although there are some gaps since the decomposition is incomplete. The results are shown in Table 1. The accuracy ranged from 75% to 90%. The table shows the number of firings of each MU that were correctly identified (TP: true positives), the number that were misidentified (FP: false positives), and the number that were missed (FN: false negatives). The relatively poor accuracy of MU 1 was due in large part to the problem that some of the active segments associated with this MU were registered on their negative peaks and others on their positive peaks. The missed firings of the other MUs were mostly due to superpositions. For comparison the actual mean firing rates calculated from the full firing patterns are also shown. Decomp Estimated Actual MU index meanfr Acc meanfr Acc #TP #FP #FN % % % % % % % % Table 1. Accuracy Assessment

7 Final Notes The signal R dat is a fairly simple signal. It contains four large MUAP trains with highly distinguishable MUAP shapes, relatively few superpositions, and fairly low background noise. This makes it ideal for illustrating the main points in this tutorial. Not all EMG signals are this simple, but some are, especially ones recorded with selective electrodes at low levels of effort. More complex signals are generally more difficult to decompose, and practical decomposition methods must deal with a number of issues not discussed here, including overlapping clusters, duplicate clusters, MUAP variability, slow changes in MUAP shape, and non-stationarity of MU firing patterns. It should be remembered that, as in any signal detection problem, the accuracy of the final results is dependent on the signal-to-noise ratio of the data. While there are many EMG signals that can be decomposed with a high degree of confidence, there are also many signals that, because of suboptimal electrode placement, excessive signal complexity, or some other factor, cannot be decomposed reliably at all. The tutorial script can also be run on the signals in other channels of R To do this, select the desired channel in EMGlab and re-run the tutorial script. The tutorial script can also be used with other signals. To do this, load the signal into EMGlab and re-run the tutorial. The script will ask for a gold standard annotation file. Click "cancel" if one is not available. Some EMG files and annotations are available on However, the tutorial script is intended for simple signals like R EMGlab The EMGlab program is an open-source signal viewer and annotation editor that contains algorithms for automatic and manual decomposition. It provides a convenient platform for developing and testing new decomposition algorithms. This tutorial uses EMGlab to load and filter the EMG signal and to display the final results. It also uses EMGlab functions to calculate the detection threshold, detect and align the active segments, and estimate the mean firing rates. References 1. Marateb HR, Muceli S, McGill KC, Merletti R, Farina D. Robust decomposition of single-channel intramuscular EMG signals at low force levels. J Neural Eng. 2011;8(6): McGill KC, Dorfman LJ. High-resolution alignment of sampled waveforms. IEEE Transactions on Biomedical Engineering. 1984;31(6): Daszykowski M, Walczak B, Massart DL. Looking for natural patterns in analytical data. 2. Tracing local density with OPTICS. Journal of Chemical Information and Computer Sciences. 2002;42(3): Epub 2002/06/28. [Software available at download/optics.m]. 4. Ankerst M, Breunig MM, Kriegel H-P, Sander R. OPTICS: ordering points to identify the clustering structure. Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data; Philadelphia, PA, USA : ACM; p Stashuk D, Qu Y. Robust method for estimating motor unit firing-pattern statistics. Medical & Biological Engineering & Computing. 1996;34(1):50-7.