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Issues and expectations for EMG decomposition
Motor Unit Number Estimation (MUNE) and Quantitative EMG (Supplements to Clinical Neurophysiology, Vol. 60) Editors: M.B. Bromberg # 2009 Elsevier B.V. All rights reserved
Chapter 23
Issues and expectations for EMG decomposition Kevin C. McGill* Rehabilitation Research and Development Center, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA
1. Introduction The process of separating out the individual trains of motor unit action potentials (MUAPs) that make up an EMG signal is referred to as EMG decomposition (Fig. 23.1). EMG decomposition is used in clinical neurophysiology to quantify MUAP waveforms for diagnosing neuromuscular disorders (Stashuk, 2001), and in basic physiology to study motor unit behavior and muscle architecture (Basmajian and De Luca, 1985; McGill et al., 2005). Decomposition provides a way to quantify clinical observations that are now largely subjective, and to study the behavior of multiple motor units (MUs) simultaneously. A number of effective computer-aided and fully automatic decomposition programs have been developed over the last 25 years (Guiheneuc et al., 1983; Gerber et al., 1984; McGill et al., 1985; Stashuk and De Bruin, 1988; Hass and Meyer, 1989; De Luca, 1993; Nandedkar et al., 1995; Fang et al., 1999; Gut and Moschytz, 2000; Zennaro *
Correspondence to: Dr. Kevin McGill, Rehabilitation Research and Development Center, VA Palo Alto Health Care System, 3801 Miranda Ave., Palo Alto, CA 94304, USA. Tel.: þ1-650-493-5000, ext. 6-4477; Fax: þ1-650-493-4919; E-mail: [email protected]
et al., 2003; De Luca et al., 2006; Kleine et al., 2006). However, despite their utility, none of these programs has yet gained widespread use outside the laboratory in which it was developed, and EMG decomposition remains little used in routine clinical practice. Some of the reasons that EMG decomposition is not more widely used include its perceived technical complexity, a lack of readily available software, a lack of agreed-upon data formats and analysis criteria, and skepticism about its accuracy and validity. This paper considers two areas in which progress might be expected to advance the acceptance and use of EMG decomposition: increased sharing of signals and algorithms between laboratories and objective methods for assessing decomposition accuracy.
2. Sharing EMG data and algorithms Greater sharing of EMG decomposition data and software between clinicians, scientists and algorithm developers would have several worthwhile benefits. Greater sharing would substantiate decomposition’s capabilities and short comings, allow peer review of and document scientific findings, provide realistic signals to guide the development and validation of new algorithms, motivate
222 EEG, and gait; Neurodatabase (http://www.neurodatabase.org), a public basic neuroscience database; the Brain Resource International Database, which contains EEG records from 50 different laboratories (Gordon and Konopka, 2005); and the Australian EEG database, which contains 18,500 EEG records (Hunter et al., 2005). Until recently the only generally available databases of EMG signals have been educational CD-ROMs (Barkhaus and Nandedkar, 1999; Ricker, 1999; Daube and Devon, 2002; Preston and Shapiro, 2005). 2.1. The EMGlab website Fig. 23.1 EMG decomposition separates an EMG signal into its constituent motor unit action potentials (MUAPs) and their firing patterns.
the development of standards, and provide material for education and training. Fins and Gardner (2003) make the case for data sharing even more strongly: “Because science is a collective enterprise, producers of data are obliged to share their products with their peers for review and utilization for the advancement of knowledge.” Data sharing places certain responsibilities on the various parties involved that are similar to the responsibilities associated with scientific publication (Fins and Gardner, 2003). The producer of the data, like the author of a paper, is responsible for providing timely access to the data, ensuring its accuracy, providing sufficient supporting information to allow its re-use, and maintaining the safety and privacy of human subjects. The sharer of the data, like the publisher of a paper, is responsible for providing access and ensuring accuracy. The user of the data, like the reader of a paper, is responsible for recognizing the contribution of the producer and for maintaining the data’ accuracy and integrity. There are currently several large databases of physiological signals. These include PhysioNet (http://www.physionet.org) a research resource for complex physiologic signals including ECG,
We recently established the website http://www. emglab.net to be a forum for sharing software, data and information related to EMG decomposition. The goals of the website are to promote decomposition as a research tool, to promote the exchange of EMG data, to encourage attention to accuracy, and to encourage algorithm innovation. The project is funded by the US National Institute of Neurological Disorders and Stroke. The EMGlab website contains a small but growing database of EMG signals. The database currently includes sample signals from five different laboratories illustrating a variety of recording techniques, muscles, and experimental conditions; synthetic signals simulating normal and pathological EMG; and a set of over 1000 signals from healthy subjects and patients with neuromuscular disorders complied by Nikolic (2001). We intend to provide “blue-ribbon” decompositions that have been checked and approved by a panel of experts for many of these signals. An essential component for data sharing is a common data format. At the current time, EMG signals recorded by commercial electromyographs or by laboratory computers are stored with a variety of sampling rates, byte orderings, and data formats. Information about sampling rate and amplitude scaling may or may not be stored along with the data. Many decomposition algorithms were written specifically for data in a
223 particular format and sampling rate and generate their results primarily in hard-copy format. This lack of standardization complicates data sharing. More than 70 different scientific data formats are currently used for storing biomedical signals (Schlo¨gl, 2006). For the EMGlab website, we considered three data formats that have been proposed as standards for physiological signals. ASTM E-1467 (standard for transferring digital neurophysiological data between independent computer systems) is a detailed standard that is now used mainly for documenting procedures in medical records (Jacobs et al., 1993; Varri et al., 2001). EDF (European data format) has become the de facto standard for exchanging EEG signals (Varri et al., 2001; Kemp and Olivan, 2003). WFDB (waveform database format) is a versatile standard used for the signals in the PhysioNet database (Physionet, 2006). The ASTM E-1467 and EDF formats combine header information (i.e., information about sampling rate, gain) and data in the same file. While efficient, this forces the data producer to comply with the format. The WFDB format, on the other hand, allows a variety of data formats, using a separate header file to describe the format of the data file. This facilitates sharing files that have already been created, putting the burden of translation primarily on the user. Since it is our goal to encourage investigators to contribute files, we chose to use the WFDB format. 2.2. The EMGlab program The EMGlab website includes a downloadable, open-source program for reading EMG signals with associated WFDB headers. The program includes a signal viewer, an automatic decomposition algorithm, and a graphical editor (McGill et al., 2005). It is written in the Matlab programming language (Mathworks). The program’s screen is divided into five panels (Fig. 23.2) that show a segment of the EMG signal, the templates of the identified MUs, their identified firing patterns, a close-up of the signal for resolving
superpositions, and a thumbnail of the entire signal. The user can decompose signals automatically by invoking the automatic algorithm or manually using the graphical interface. The program makes it possible to look at signals and results at different levels of detail, from the discharge-to-discharge variability of individual MUAP components (Fig. 23.3), to the long-term coordinated firing behavior of groups of MUs (Fig. 23.4). 3. Decomposition accuracy Because EMG decomposition involves complicated spike identification algorithms, physiological assumptions about the likelihood of alternative possibilities, and sometimes subjective decisions made by human operators, it is not easy to assess the accuracy of the results. Nevertheless, as for any scientific methodology, it is important to develop rigorous and objective methods to do so. Previous efforts to assess decomposition accuracy have focused primarily on measuring the average performance of a decomposition program over some group of signals. However, a program can achieve different levels of accuracy for different signals and even for different MUAPs in the same signal. Therefore, in order to judge the scientific validity of a particular result it is necessary to know the accuracy of that particular result. It is thus incumbent on investigators and algorithm developers to try to estimate decomposition accuracy individually for every MUAP train in every signal. Accuracy can be quantified in a variety of ways. Specific indices such as sensitivity, specificity and confusion are useful for algorithm developers who are interested in comparing the performance of different algorithms (Farina et al., 2001a; Carrey and Clancy, 2005). However, for investigators who are primarily interested in the scientific validity of a particular firing pattern, a single index is more convenient. This index should equal 100% if all the discharges in the firing pattern are detected correctly, and it should be reduced for every missed discharge and false positive (Fig. 23.5).
224
Fig. 23.2 EMGlab screen. The top panel shows a segment of the EMG signal (high-pass filtered at 1250 Hz), the numbers of the identified MUAPs, the expected firing time of a selected MU (here MU 1), and the residual signal that remains after the templates of the identified MUAPs have been subtracted from the signal. The second panel shows the MUAP templates. The third and fourth panels show the identified firing patterns and a close-up of the signal for resolving superpositions. The bottom panel shows a thumbnail of the entire signal.
Fig. 23.3 Decomposition makes it possible to subtract out obstructing interference. (A) Two segments of an EMG signal from the brachioradialis muscle. (B) The interaction between MUs 1 and 2 can be observed more clearly with the activity of the other MUs subtracted out. The interaction here is due to a doubly innervated muscle fiber (Lateva et al., 2002).
Fig. 23.4 Decomposition makes it possible to study the coordinated activity of groups of MUs. Here the instantaneous firing rates of 18 co-active MUs in the brachioradialis muscle can be seen to fluctuate with a high degree of coherence.
225
Fig. 23.5 Quantifying decomposition accuracy. The estimated firing pattern of MU 1 contains 4 correct identifications (o) and 3 errors (x): a miss, a near miss (estimated discharge time incorrect by more than 0.5 ms but less than 5 ms), and a false positive.
3.1. Decomposition precision The criterion for deciding whether a discharge has been identified correctly depends on the error between the estimated and actual firing times. Because of physiological uncertainty, a MU’s actual firing parameters cannot be known with unlimited precision. There can be an uncertainty of as much as 0.05 ms in the actual time of occurrence of a MUAP because of the jitter, or “jiggle,” between the individual single fiber action potentials that make it up (Sta˚lberg, 1994). There can be a difference of as much as 0.5 ms in the duration of an interdischarge interval at different locations along the MU axis because of discharge-to-discharge variation in muscle fiber conduction velocity. The relative timing of the discharges of the motoneurons in the spinal cord can differ from the relative timing of the MUAPs by as much as 5 ms due to differences in axonal conduction velocities. The precision with which discharge times can be estimated in decomposition is limited by measurement error. The sharpest MUAPs recorded by conventional concentric or monopolar needle electrodes can only be localized to within about 0.005 ms because of baseline noise. Small MUAPs involved in superpositions often cannot be located any more precisely than to within 2 ms.
A reasonable criterion to use for classifying identifications as correct or incorrect is 0.5 ms (Farina et al., 2001a). An error of less than 5 ms can be counted as a single “near-miss” error rather than as two separate errors (a miss and a false positive; Fig. 23.5). 3.2. Methods for assessing accuracy Four main methods have been used for assessing decomposition accuracy. The first method is to check the results by manual inspection. This approach is based on the assumption that an experienced human operator can determine the correct decomposition more reliably than an automatic algorithm. While this is often the case, this approach is not completely rigorous, since even experienced human operators can make mistakes, especially in complex signals. A second method for assessing accuracy is to use synthetic signals whose true composition is known (Farina et al., 2001b; Hamilton-Wright and Stashuk, 2005). This approach is often used in validation studies to assess algorithm performance. However, it is only valid to the extent that all the factors that affect decomposition accuracy are realistically simulated. Moreover, this approach cannot be used directly to assess the decomposition accuracy of real signals whose true composition is not known.
226 A third method for assessing accuracy is to examine the self-consistency of the results. Shimmer plots (Doherty and Stashuk, 2003) can provide confidence in the physiological validity of the identified MUAP waveforms by showing their consistency (Fig. 23.6A). It is not possible to determine from these plots, however, whether every MU in the signal was identified. Inter-discharge interval histograms (Doherty and Stashuk, 2003) and plots of instantaneous firing rate can show whether the identified firing patterns are physiologically realistic (Fig. 23.6B). However, these plots cannot be considered definitive proofs of decomposition accuracy. In theory, it should be possible to determine the accuracy of a decomposition solely from evidence presented by the signal itself. For example, the decomposition in Fig. 23.7 is almost certainly complete and correct. It explains the signal down to the level of the baseline noise by a set of MUAPs with physiologically realistic firing patterns. Any other arrangement of the templates would results in a much larger residual. This idea can be formalized in terms of the decomposition’s a posteriori probability, which is the probability that the decomposition is correct out of all other possible decompositions. This approach can be used for simple signals, but for complex and noisy signals it becomes computationally impractical to consider all the different possible arrangements. The fourth method for assessing decomposition accuracy is to cross-check results obtained from two signals recorded simultaneously from nearby sites in the same muscle (Mambrito and De Luca,
Fig. 23.6 (A) Shimmer plots showing several superimposed occurrences of 4 identified MUs. The consistency of the MUAP waveforms confirms their physiological validity. (B) Interdischarge interval (IDI) histograms of the MUs. Unimodal histograms are an indication of regular firing patterns.
1984; McGill et al., 2004) (Fig. 23.8). Some of the MUs seen in each signal will also be seen in the other, although the sizes and shapes of the MUAPs and the background activity will be different. The two signals are decomposed independently, and
Fig. 23.7 Simple EMG signal in which 3 MUAP trains have been identified. This decomposition can be considered complete and correct because of the unlikelihood that any other configuration of the templates could result in such a small residual.
227
Fig. 23.8 Assessing decomposition accuracy by cross-checking. The two signals were recorded simultaneously from nearby sites in the same muscle and decomposed independently. Some MUs were seen in both signals (e.g., MU 4 in signal 1 ¼ MU 3 in signal 2), while others were only seen in one signal but not the other (e.g., MU 1 in signal 2). The two decompositions agreed on the timing of many discharges to within 0.5 ms (after taking into account a possible fixed offset) (dotted lines), but disagreed on others (solid lines).
the results are compared. Any MUAP trains that are found to be time-locked are presumed to correspond to the same MU. Whenever the two decompositions agree on the precise timing of a particular discharge, that time can be presumed to be correct, since otherwise both decompositions would have to be in error by precisely the same amount, which is highly unlikely. On the other hand, whenever the two decompositions disagree, then at least one of them must be wrong. By counting up the number of agreements and disagreements it is possible to compute a lower bound on the decomposition accuracy of the train. The cross-checking approach provides an objective way to assess decomposition accuracy for actual signals. Its disadvantages are that it requires multiple recordings and multiple decompositions for each contraction, and that it can only be used for MUs that are seen in both signals. In practice, cross-checking is not feasible for every signal. An alternative is to cross-check a representative subset of signals and use the results to estimate accuracy in the other signals (McGill et al., 2004). (The estimation could also be based on simulated signals if they are truly representative of the actual ones.) Since decomposition accuracy depends on MUAP distinguishability and signal complexity, the estimation
should be based on some measure of MUAP signal-to-noise ratio that takes these characteristics into account (Fig. 23.9). 4. Conclusions EMG decomposition is potentially a powerful tool for investigating the health and function of the neuromuscular system. It is hoped that
Fig. 23.9 Estimating decomposition accuracy. Crosscheck agreement rates for 83 MUs from 10 pairs of EMG signals plotted against MU signal-to-noise ratio (SNR). The SNR was defined as the Euclidean distance between the MUAP and the next most similar MUAP divided by the rms amplitude of the signal, in the signal in which the MU is smaller. The solid line can be used to predict decomposition accuracy for MUAPs in other similar signals.
228 greater sharing of decomposition data, along with a frank assessment of decomposition accuracy, will lead to better understanding of the validity and capabilities of this technique. Acknowledgements Edward Clancy, Hamid Marateb, Zhelyasko Tumbev and Zoia Lateva made major contributions to the philosophy, design, content, and implementation of the EMGlab website and decomposition program. Daniel Stashuk, Miki Nikolic, Gary Kamen, Johannes Van Dijk, Pierre Mathieu and Andrew Hamilton-Wright have contributed data files. This work was funded by the US Department of Veterans Affairs and the US National Institute of Neurological Disorders and Stroke. References Barkhaus, P.E. and Nandedkar, S.D. (1999) Electronic Atlas of Electromyographic Waveforms EMG on CD. CASA, New York. Basmajian, J.V. and De Luca, C.J. (1985) Muscles Alive. Their Functions Revealed by Electromyography, 5th Edn.Williams and Wilkins, Baltimore, MD. Carey, R.M. and Clancy, E.A. (2005) EMG decomposition annotation comparison method. In: Proc. IEEE 31st Ann. Northeast Bioeng. Conf. IEEE, New York, pp. 100–101. Daube, J. and Devon, R. (2002) The Sounds of EMG (CD-Rom). Nicolet Viasys Healthcare, Madison, WI. De Luca, C.J. (1993) Precision decomposition of EMG signals. Meth. Clin. Neurophysiol., 4: 1–28. De Luca, C.J., Adam, A., Wotiz, R., Gilmore, L.D. and Nawab, S.H. (2006) Decomposition of surface EMG signals. J. Neurophysiol., 96: 1646–1657. Doherty, T.J. and Stashuk, D.W. (2003) Decomposition-based quantitative electromyography: methods and initial normative data in five muscles. Muscle Nerve, 28: 204–211. Fang, J., Agarwal, G.C. and Shahani, B.T. (1999) Decomposition of multiunit electromyographic signals. IEEE Trans. Biomed. Eng., 46: 685–697. Farina, D., Colombo, R., Merletti, R. and Olsen, H.B. (2001a) Evaluation of intra-muscular EMG signal decomposition algorithms. J. Electromyogr. Kinesiol., 11: 175–187. Farina, D., Crosetti, A. and Merletti, R. (2001b) A model for the generation of synthetic intramuscular EMG signals to test decomposition algorithms. IEEE Trans. Biomed. Eng., 48: 66–77. Fins, J.J. and Gardner, D. (2003) Data sharing IV: Ethics of data sharing. http://datasharing.net/DS-4–2003-poster.pdf.
Gerber, A., Studer, R.M., De Figueiredo, R.J.P. and Moschytz, G.S. (1984) A new framework and computer program for quantitative EMG signal analysis. IEEE Trans. Biomed. Eng., BME-31: 285–295. Gordon, E. and Konopka, L.M. (2005) EEG databases in research and clinical practice: current status and future directions. Clin. EEG Neurosci., 36: 53–54. Guiheneuc, P., Calamel, J., Doncarli, C., Gitton, D. and Michel, C. (1983) Automatic detection and pattern recognition of single motor unit potentials in needle EMG. In: J.E. Desmedt (Ed.), Computer Aided Electromyography. Karger, Basel. Gut, R. and Moschytz, G.S. (2000) High-precision EMG signal decomposition using communication techniques. IEEE Trans. Signal Proc., 48: 2487–2494. Haas, W.F. and Meyer, M. (1989) An automatic EMG decomposition system for routine clinical examination and clinical research — ARTMUP. In: J.E. Desmedt (Ed.), ComputerAided Electromyography and Expert Systems. Elsevier Science, Amsterdam. Hamilton-Wright, A. and Stashuk, D.W. (2005) Physiologically based simulation of clinical EMG signals. IEEE Trans. Biomed. Eng., 52: 171–183. Hunter, M., Smith, R.L. and Hyslop, W. et al. (2005) The Australian EEG database. Clin. EEG Neurosci., 36: 76–81. Jacobs, E.C., Lagerlund, T.D., Collura, T.F. and Burgess, R.C. (1993) Data interchange for clinical neurophysiology. Stud. Health Technol. Inform., 6: 195–202. Kemp, B. and Olivan, J. (2003) European data format ‘plus’ (EDFþ), an EDF alike standard format for the exchange of physiological data. Clin. Neurophysiol., 114: 1755–1761. Kleine, B.U., Van Dijk, J.P., Lapatki, B.G., Zwarts, M.J. and Stegeman, D.F. (2006) Using two-dimensional spatial information in decomposition of surface EMG signals. J. Electromyogr. Kinesiol., 17: 535–548. Lateva, Z.C., McGill, K.C. and Johanson, M.E. (2002) Electrophysiological evidence of adult human skeletal muscle fibres with multiple endplates and polyneuronal innervation. J. Physiol. (Lond.), 544(Pt. 2): 549–565. Mambrito, B. and De Luca, C.J. (1984) A technique for the detection, decomposition and analysis of the EMG signal. Electroencephalogr. Clin. Neurophysiol., 58: 175–188. McGill, K.C., Cummins, K.L. and Dorfman, L.J. (1985) Automatic decomposition of the clinical electromyogram. IEEE Trans. Biomed. Eng., BME-32: 470–477. McGill, K.C., Lateva, Z.C. and Johanson, M.E. (2004) Validation of a computer-aided EMG decomposition method. In: Proc. 26th Ann. Intl. Conf. IEEE Eng. Med. Biol. Soc., IEEE, New York, pp. 4744–4747. McGill, K.C., Lateva, Z.C. and Marateb, H.R. (2005) EMGLAB: an interactive EMG decomposition program. J. Neurosci. Meth., 149: 121–133. Nandedkar, S.D., Barkhaus, P.E. and Charles, A. (1995) Multi-motor unit action potential analysis (MMA). Muscle Nerve, 18: 1155–1166. Nikolic, M. (2001) Detailed Analysis of Clinical Electromyography Signals EMG Decomposition, Findings and Firing Pattern Analysis in Controls and Patients with Myopathy
229 and Amytrophic Lateral Sclerosis. PhD Thesis, Faculty of Health Science, University of Copenhagen, Copenhagen. Physionet (2006) The WFDB Software Package. http://www.physionet.org/physiotools/wfdb.shtml. Preston, D.C. and Shapiro, B.E. (2005) Electromyography and Neuromuscular Disorders: Clinical–Electrophysiological Correlations (with CD-Rom), 2nd Edn. Butterworth-Heinemann, Philadelphia, PA. Ricker, K. (1999) EMG Training (CD-Rom). Jaeger, Toennies. Schlo¨gl, A. (2006) The BioSig Project. http://www.biosig.sourceforge.net. Sta˚lberg, E.V. and Sonoo, M. (1994) Assessment of variability in the shape of the motor unit action potential, the “jiggle,” at consecutive discharges. Muscle Nerve, 17: 1135–1144.
Stashuk, D. (2001) EMG signal decomposition: how can it be accomplished and used? J. Electromyogr. Kinesiol., 11: 151–173. Stashuk, D. and De Bruin, H. (1988) Automatic decomposition of selective needle-detected myoelectric signals. IEEE Trans. Biomed. Eng., 35: 1–10. Varri, A., Kemp, B., Penzel, T. and Schlogl, A. (2001) Standards for biomedical signal databases. IEEE Eng. Med. Biol. Magn., 20: 33–37. Zennaro, D., Wellig, P., Koch, V.M., Moschytz, G.S. and Laubli, T. (2003) A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients. IEEE Trans. Biomed. Eng., 50: 58–69.
Chapter 23
Issues and expectations for EMG decomposition Kevin C. McGill* Rehabilitation Research and Development Center, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA
1. Introduction The process of separating out the individual trains of motor unit action potentials (MUAPs) that make up an EMG signal is referred to as EMG decomposition (Fig. 23.1). EMG decomposition is used in clinical neurophysiology to quantify MUAP waveforms for diagnosing neuromuscular disorders (Stashuk, 2001), and in basic physiology to study motor unit behavior and muscle architecture (Basmajian and De Luca, 1985; McGill et al., 2005). Decomposition provides a way to quantify clinical observations that are now largely subjective, and to study the behavior of multiple motor units (MUs) simultaneously. A number of effective computer-aided and fully automatic decomposition programs have been developed over the last 25 years (Guiheneuc et al., 1983; Gerber et al., 1984; McGill et al., 1985; Stashuk and De Bruin, 1988; Hass and Meyer, 1989; De Luca, 1993; Nandedkar et al., 1995; Fang et al., 1999; Gut and Moschytz, 2000; Zennaro *
Correspondence to: Dr. Kevin McGill, Rehabilitation Research and Development Center, VA Palo Alto Health Care System, 3801 Miranda Ave., Palo Alto, CA 94304, USA. Tel.: þ1-650-493-5000, ext. 6-4477; Fax: þ1-650-493-4919; E-mail: [email protected]
et al., 2003; De Luca et al., 2006; Kleine et al., 2006). However, despite their utility, none of these programs has yet gained widespread use outside the laboratory in which it was developed, and EMG decomposition remains little used in routine clinical practice. Some of the reasons that EMG decomposition is not more widely used include its perceived technical complexity, a lack of readily available software, a lack of agreed-upon data formats and analysis criteria, and skepticism about its accuracy and validity. This paper considers two areas in which progress might be expected to advance the acceptance and use of EMG decomposition: increased sharing of signals and algorithms between laboratories and objective methods for assessing decomposition accuracy.
2. Sharing EMG data and algorithms Greater sharing of EMG decomposition data and software between clinicians, scientists and algorithm developers would have several worthwhile benefits. Greater sharing would substantiate decomposition’s capabilities and short comings, allow peer review of and document scientific findings, provide realistic signals to guide the development and validation of new algorithms, motivate
222 EEG, and gait; Neurodatabase (http://www.neurodatabase.org), a public basic neuroscience database; the Brain Resource International Database, which contains EEG records from 50 different laboratories (Gordon and Konopka, 2005); and the Australian EEG database, which contains 18,500 EEG records (Hunter et al., 2005). Until recently the only generally available databases of EMG signals have been educational CD-ROMs (Barkhaus and Nandedkar, 1999; Ricker, 1999; Daube and Devon, 2002; Preston and Shapiro, 2005). 2.1. The EMGlab website Fig. 23.1 EMG decomposition separates an EMG signal into its constituent motor unit action potentials (MUAPs) and their firing patterns.
the development of standards, and provide material for education and training. Fins and Gardner (2003) make the case for data sharing even more strongly: “Because science is a collective enterprise, producers of data are obliged to share their products with their peers for review and utilization for the advancement of knowledge.” Data sharing places certain responsibilities on the various parties involved that are similar to the responsibilities associated with scientific publication (Fins and Gardner, 2003). The producer of the data, like the author of a paper, is responsible for providing timely access to the data, ensuring its accuracy, providing sufficient supporting information to allow its re-use, and maintaining the safety and privacy of human subjects. The sharer of the data, like the publisher of a paper, is responsible for providing access and ensuring accuracy. The user of the data, like the reader of a paper, is responsible for recognizing the contribution of the producer and for maintaining the data’ accuracy and integrity. There are currently several large databases of physiological signals. These include PhysioNet (http://www.physionet.org) a research resource for complex physiologic signals including ECG,
We recently established the website http://www. emglab.net to be a forum for sharing software, data and information related to EMG decomposition. The goals of the website are to promote decomposition as a research tool, to promote the exchange of EMG data, to encourage attention to accuracy, and to encourage algorithm innovation. The project is funded by the US National Institute of Neurological Disorders and Stroke. The EMGlab website contains a small but growing database of EMG signals. The database currently includes sample signals from five different laboratories illustrating a variety of recording techniques, muscles, and experimental conditions; synthetic signals simulating normal and pathological EMG; and a set of over 1000 signals from healthy subjects and patients with neuromuscular disorders complied by Nikolic (2001). We intend to provide “blue-ribbon” decompositions that have been checked and approved by a panel of experts for many of these signals. An essential component for data sharing is a common data format. At the current time, EMG signals recorded by commercial electromyographs or by laboratory computers are stored with a variety of sampling rates, byte orderings, and data formats. Information about sampling rate and amplitude scaling may or may not be stored along with the data. Many decomposition algorithms were written specifically for data in a
223 particular format and sampling rate and generate their results primarily in hard-copy format. This lack of standardization complicates data sharing. More than 70 different scientific data formats are currently used for storing biomedical signals (Schlo¨gl, 2006). For the EMGlab website, we considered three data formats that have been proposed as standards for physiological signals. ASTM E-1467 (standard for transferring digital neurophysiological data between independent computer systems) is a detailed standard that is now used mainly for documenting procedures in medical records (Jacobs et al., 1993; Varri et al., 2001). EDF (European data format) has become the de facto standard for exchanging EEG signals (Varri et al., 2001; Kemp and Olivan, 2003). WFDB (waveform database format) is a versatile standard used for the signals in the PhysioNet database (Physionet, 2006). The ASTM E-1467 and EDF formats combine header information (i.e., information about sampling rate, gain) and data in the same file. While efficient, this forces the data producer to comply with the format. The WFDB format, on the other hand, allows a variety of data formats, using a separate header file to describe the format of the data file. This facilitates sharing files that have already been created, putting the burden of translation primarily on the user. Since it is our goal to encourage investigators to contribute files, we chose to use the WFDB format. 2.2. The EMGlab program The EMGlab website includes a downloadable, open-source program for reading EMG signals with associated WFDB headers. The program includes a signal viewer, an automatic decomposition algorithm, and a graphical editor (McGill et al., 2005). It is written in the Matlab programming language (Mathworks). The program’s screen is divided into five panels (Fig. 23.2) that show a segment of the EMG signal, the templates of the identified MUs, their identified firing patterns, a close-up of the signal for resolving
superpositions, and a thumbnail of the entire signal. The user can decompose signals automatically by invoking the automatic algorithm or manually using the graphical interface. The program makes it possible to look at signals and results at different levels of detail, from the discharge-to-discharge variability of individual MUAP components (Fig. 23.3), to the long-term coordinated firing behavior of groups of MUs (Fig. 23.4). 3. Decomposition accuracy Because EMG decomposition involves complicated spike identification algorithms, physiological assumptions about the likelihood of alternative possibilities, and sometimes subjective decisions made by human operators, it is not easy to assess the accuracy of the results. Nevertheless, as for any scientific methodology, it is important to develop rigorous and objective methods to do so. Previous efforts to assess decomposition accuracy have focused primarily on measuring the average performance of a decomposition program over some group of signals. However, a program can achieve different levels of accuracy for different signals and even for different MUAPs in the same signal. Therefore, in order to judge the scientific validity of a particular result it is necessary to know the accuracy of that particular result. It is thus incumbent on investigators and algorithm developers to try to estimate decomposition accuracy individually for every MUAP train in every signal. Accuracy can be quantified in a variety of ways. Specific indices such as sensitivity, specificity and confusion are useful for algorithm developers who are interested in comparing the performance of different algorithms (Farina et al., 2001a; Carrey and Clancy, 2005). However, for investigators who are primarily interested in the scientific validity of a particular firing pattern, a single index is more convenient. This index should equal 100% if all the discharges in the firing pattern are detected correctly, and it should be reduced for every missed discharge and false positive (Fig. 23.5).
224
Fig. 23.2 EMGlab screen. The top panel shows a segment of the EMG signal (high-pass filtered at 1250 Hz), the numbers of the identified MUAPs, the expected firing time of a selected MU (here MU 1), and the residual signal that remains after the templates of the identified MUAPs have been subtracted from the signal. The second panel shows the MUAP templates. The third and fourth panels show the identified firing patterns and a close-up of the signal for resolving superpositions. The bottom panel shows a thumbnail of the entire signal.
Fig. 23.3 Decomposition makes it possible to subtract out obstructing interference. (A) Two segments of an EMG signal from the brachioradialis muscle. (B) The interaction between MUs 1 and 2 can be observed more clearly with the activity of the other MUs subtracted out. The interaction here is due to a doubly innervated muscle fiber (Lateva et al., 2002).
Fig. 23.4 Decomposition makes it possible to study the coordinated activity of groups of MUs. Here the instantaneous firing rates of 18 co-active MUs in the brachioradialis muscle can be seen to fluctuate with a high degree of coherence.
225
Fig. 23.5 Quantifying decomposition accuracy. The estimated firing pattern of MU 1 contains 4 correct identifications (o) and 3 errors (x): a miss, a near miss (estimated discharge time incorrect by more than 0.5 ms but less than 5 ms), and a false positive.
3.1. Decomposition precision The criterion for deciding whether a discharge has been identified correctly depends on the error between the estimated and actual firing times. Because of physiological uncertainty, a MU’s actual firing parameters cannot be known with unlimited precision. There can be an uncertainty of as much as 0.05 ms in the actual time of occurrence of a MUAP because of the jitter, or “jiggle,” between the individual single fiber action potentials that make it up (Sta˚lberg, 1994). There can be a difference of as much as 0.5 ms in the duration of an interdischarge interval at different locations along the MU axis because of discharge-to-discharge variation in muscle fiber conduction velocity. The relative timing of the discharges of the motoneurons in the spinal cord can differ from the relative timing of the MUAPs by as much as 5 ms due to differences in axonal conduction velocities. The precision with which discharge times can be estimated in decomposition is limited by measurement error. The sharpest MUAPs recorded by conventional concentric or monopolar needle electrodes can only be localized to within about 0.005 ms because of baseline noise. Small MUAPs involved in superpositions often cannot be located any more precisely than to within 2 ms.
A reasonable criterion to use for classifying identifications as correct or incorrect is 0.5 ms (Farina et al., 2001a). An error of less than 5 ms can be counted as a single “near-miss” error rather than as two separate errors (a miss and a false positive; Fig. 23.5). 3.2. Methods for assessing accuracy Four main methods have been used for assessing decomposition accuracy. The first method is to check the results by manual inspection. This approach is based on the assumption that an experienced human operator can determine the correct decomposition more reliably than an automatic algorithm. While this is often the case, this approach is not completely rigorous, since even experienced human operators can make mistakes, especially in complex signals. A second method for assessing accuracy is to use synthetic signals whose true composition is known (Farina et al., 2001b; Hamilton-Wright and Stashuk, 2005). This approach is often used in validation studies to assess algorithm performance. However, it is only valid to the extent that all the factors that affect decomposition accuracy are realistically simulated. Moreover, this approach cannot be used directly to assess the decomposition accuracy of real signals whose true composition is not known.
226 A third method for assessing accuracy is to examine the self-consistency of the results. Shimmer plots (Doherty and Stashuk, 2003) can provide confidence in the physiological validity of the identified MUAP waveforms by showing their consistency (Fig. 23.6A). It is not possible to determine from these plots, however, whether every MU in the signal was identified. Inter-discharge interval histograms (Doherty and Stashuk, 2003) and plots of instantaneous firing rate can show whether the identified firing patterns are physiologically realistic (Fig. 23.6B). However, these plots cannot be considered definitive proofs of decomposition accuracy. In theory, it should be possible to determine the accuracy of a decomposition solely from evidence presented by the signal itself. For example, the decomposition in Fig. 23.7 is almost certainly complete and correct. It explains the signal down to the level of the baseline noise by a set of MUAPs with physiologically realistic firing patterns. Any other arrangement of the templates would results in a much larger residual. This idea can be formalized in terms of the decomposition’s a posteriori probability, which is the probability that the decomposition is correct out of all other possible decompositions. This approach can be used for simple signals, but for complex and noisy signals it becomes computationally impractical to consider all the different possible arrangements. The fourth method for assessing decomposition accuracy is to cross-check results obtained from two signals recorded simultaneously from nearby sites in the same muscle (Mambrito and De Luca,
Fig. 23.6 (A) Shimmer plots showing several superimposed occurrences of 4 identified MUs. The consistency of the MUAP waveforms confirms their physiological validity. (B) Interdischarge interval (IDI) histograms of the MUs. Unimodal histograms are an indication of regular firing patterns.
1984; McGill et al., 2004) (Fig. 23.8). Some of the MUs seen in each signal will also be seen in the other, although the sizes and shapes of the MUAPs and the background activity will be different. The two signals are decomposed independently, and
Fig. 23.7 Simple EMG signal in which 3 MUAP trains have been identified. This decomposition can be considered complete and correct because of the unlikelihood that any other configuration of the templates could result in such a small residual.
227
Fig. 23.8 Assessing decomposition accuracy by cross-checking. The two signals were recorded simultaneously from nearby sites in the same muscle and decomposed independently. Some MUs were seen in both signals (e.g., MU 4 in signal 1 ¼ MU 3 in signal 2), while others were only seen in one signal but not the other (e.g., MU 1 in signal 2). The two decompositions agreed on the timing of many discharges to within 0.5 ms (after taking into account a possible fixed offset) (dotted lines), but disagreed on others (solid lines).
the results are compared. Any MUAP trains that are found to be time-locked are presumed to correspond to the same MU. Whenever the two decompositions agree on the precise timing of a particular discharge, that time can be presumed to be correct, since otherwise both decompositions would have to be in error by precisely the same amount, which is highly unlikely. On the other hand, whenever the two decompositions disagree, then at least one of them must be wrong. By counting up the number of agreements and disagreements it is possible to compute a lower bound on the decomposition accuracy of the train. The cross-checking approach provides an objective way to assess decomposition accuracy for actual signals. Its disadvantages are that it requires multiple recordings and multiple decompositions for each contraction, and that it can only be used for MUs that are seen in both signals. In practice, cross-checking is not feasible for every signal. An alternative is to cross-check a representative subset of signals and use the results to estimate accuracy in the other signals (McGill et al., 2004). (The estimation could also be based on simulated signals if they are truly representative of the actual ones.) Since decomposition accuracy depends on MUAP distinguishability and signal complexity, the estimation
should be based on some measure of MUAP signal-to-noise ratio that takes these characteristics into account (Fig. 23.9). 4. Conclusions EMG decomposition is potentially a powerful tool for investigating the health and function of the neuromuscular system. It is hoped that
Fig. 23.9 Estimating decomposition accuracy. Crosscheck agreement rates for 83 MUs from 10 pairs of EMG signals plotted against MU signal-to-noise ratio (SNR). The SNR was defined as the Euclidean distance between the MUAP and the next most similar MUAP divided by the rms amplitude of the signal, in the signal in which the MU is smaller. The solid line can be used to predict decomposition accuracy for MUAPs in other similar signals.
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