1. DQEMG: Decomposition-based Quantitative EMG Daniel W. Stashuk, PhD University of Waterloo Timothy J. Doherty MD, PhD, FRCPC The University of Western Ontario Copyright Daniel W. Stashuk and Timothy J. Doherty, 2007. Some rights reserved. Content in this presentation is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. This license is more fully described at: http://creativecommons.org/licenses/by-nc-sa/3.0/http://creativecommons.org/licenses/by-nc-sa/3.0/.

2. Objectives ► Brief review of quantitative EMG ► Principles of decomposition EMG ► Basic overview of DQEMG ► How can DQEMG be used for clinical and research applications ► Validation and reliability of the method

5. Important Neuromuscular Information Numbers of MUs Relative sizes of MUs Morphology of MUs Recruitment of MUs Firing patterns of MUs Functional stability of neuromuscular junctions

6. Why Develop Quantitative EMG Methods? Objectivity Increased Sensitivity Increased Specificity Ability to determine degree of involvement Improved ability provide longitudinal assessments

7. Quantitative EMG methods ► Single Fiber EMG ► Quantitative MUP analysis  Semi-automated MUP analysis  Automated MUP analysis ► Interference pattern analysis ► Motor unit number estimation

8. Quantitative EMG methods ► Single Fiber EMG ► Quantitative MUP analysis  Semi-automated MUP analysis  Automated MUP analysis ► Interference pattern analysis ► Motor unit number estimation

9. What is an EMG Signal? F MFP - muscle fibre potential F MUP - motor unit potential F MUPT - motor unit potential train F Composite EMG

10. EMG Signal Composition

11. MUPT - components of a MUPT

12. What is an Electromyographic (EMG) Signal?

13. Example Composite EMG Signal

14. Concepts of EMG Signal Decomposition

15. Basic Assumptions : F Each MUP can be detected F Each detected MUP can be recognized Basic Requirements : F Common MUP feature is available for detection F MUPs within the same MUPT are more similar than MUPs from different MUPTs F Typical MUPs can be determined for each MUPT (i.e., MUPs must occur in isolation) How can an EMG Signal Be Decomposed?

16. Steps in EMG Signal Decomposition F Signal Acquisition F Segmentation (Detecting MUPs) F Feature Extraction F Clustering of Detected MUPs F Supervised Classification of Detected MUPs F Discovering MUPT Temporal Relationships F Resolving Superimposed MUPs

17. Steps In Signal Decomposition

20. DQEMG ► Performs multiple passes through an EMG signal to complete a partial decomposition ► Detection pass  absolute or relative criteria  multiply and disparately detected MUPs ► Clustering pass  STBC (shape and temporal based clustering)  Analyzes a selected portion of the signal (3 to 8 s long) ► Multiple Supervised Classification Passes  Certainty-based classification  Analyzes complete signal  Robustly and actively uses firing pattern information ► Temporal relationships pass  accounts for multiply (linked MUPTs) and disparately (exclusive MUPTs) detected MUPs ► Superimposed MUPs are not resolved

21. DQEMG ► Concentric or mono-polar needle and surface or “macro” EMG signals acquired ► 30 - 60 sec isometric contractions ► Mild to moderate intensity ► Twenty or more MUs from 4 – 6 contractions

22. Acquiring EMG Signals ► Apply surface electrode configuration as per standard motor study with active electrode over motor point. ► Acquire maximal CMAP

23. Acquiring EMG Signals

24. ► Apply surface electrode configuration as per standard motor study with active electrode over motor point. ► Acquire maximal CMAP ► Perform MVC and calculate MVCRMS

25. Acquiring EMG Signals

26. ► Apply surface electrode configuration as per standard motor study with active electrode over motor point. ► Acquire maximal CMAP ► Perform MVC and calculate MVCRMS ► Insert needle electrode and position for sufficient signal quality (signal quality monitor V/s or kV/s 2 ). ► Have subject isometrically contract to desired level of effort or signal intensity (%MVCRMS effort and pps intensity monitors)

27. Acquiring EMG Signals

28. ► Apply surface electrode configuration as per standard motor study with active electrode over motor point. ► Acquire maximal CMAP ► Perform MVC and calculate MVCRMS ► Insert needle electrode and position for sufficient signal quality (signal quality monitor V/s or kV/s 2 ). ► Have subject isometrically contract to desired level of effort or signal intensity (%MVCRMS effort and pps intensity monitors) ► Decompose needle acquired signal and calculate available SMUPs ► Reposition needle and repeat during a subsequent contraction ► Continue until sufficient number of SMUPs acquired (20 –35)

29. Acquiring EMG Signals

32. Quantitative MUP analysis ► Buchthal method – 1950’s ► Concentic needle MUPs collected one-by-one from minimally contracting muscle  Slow  ++ patient and operator interaction  Biased population of MUPs  Relatively limited information provided – only MUP parameters

33. Quantitative Needle EMG Morphological parameters - prototypical MUP of each MUPT l duration, number of phases, turns, V pp, area, area/amplitude ratio

36. Spike-triggered Averaging

40. Motor Unit Number Estimation Size of M-potential Mean S-MUP Size = MUNE McComas et al. 1971

41. Mean S-MUP templateMUNE CMAP

43. Measurement of MUP Stability

44. Jitter Measurement Jitter MCD: 17.9  S Blocking: 0% Mean IPI: 219  S

47. Methods of acquiring sample of S-MUPs Incremental Stimulation MPS STA Statistical

49. Quantitative Needle EMG

50. Decomposition-Based S-MUPs

52. Decomposition-based mS-MUAP

56. Size Principle MUs with smaller twitch tensions and slower contraction times are recruited before larger, faster MUs Will this impact the sizes of MUPs collected with D-QEMG?

57. Effect of force on needle detected MUP and surface MUP size Boe et al. 2005 Muscle and Nerve

58. Biceps Force-EMG Relationships Data from 2 ALS subjects, r values of 0.96 (top) and 0.99 (bottom) Data from 2 control subjects, r values of 0.99 (top) and 0.99 (bottom)

59. Force-EMG Relationships Pearson Correlation ( r ) SubjectBicepsFDI 10.98620.9305 20.99240.9621 30.99200.7793 40.97290.9418 50.97190.9847 60.98050.7332 70.98590.7484 80.95540.9845 90.96280.9864 100.96630.8942 Minimum0.95540.7332 Maximum0.99240.9847 Mean0.97660.8945

60. Application of DQEMG to the study of Aging

61. McNeil, Doherty, Stashuk, Rice (2005) Motor unit number estimates in the tibialis anterior muscle of young, old and very old men. Muscle and Nerve. 31: 461-67 ► Does progressive MU loss contribute to loss of strength in the very old? ► Measured strength, muscle mass, MUNEs in the tibialis anterior muscle of three groups of men ► Age Groups  Young 27  3 yrs  Old 66  3 yrs  Very Old 82  4 yrs

62. Maximum M Mean S-MUP Size McNeil et al. 2004 Muscle and Nerve MVIC strength Young – 39 Nm Old – 38 Nm Very old – 30 Nm * *

63. TA MUNEs Loss of muscle Mass and Strength Functional Decline * **

64. DQEMG in CMTX ► CMTX is the X-linked form of hereditary motor-sensory neuropathy – 2 nd most common following CMT1A ► Numerous mutations of the GJβ1 gene leading to abnormalities in Connexin-32 protein ► Distal wasting and weakness by 3 rd decade in males – females usually milder course ► Conduction slowing in intermediate range (30 - 40 m/s) ► Reduced motor and sensory amplitudes ► As part of a longitudinal study we have examined the hypothenar and biceps/brachialis muscles of 58 subjects at baseline with D-QEMG

67. CMT-X ► Biceps/brachialis Controls  CMAP NP amp6.6 ± 3.6 ( 12 ± 2)  S-MUP NP amp45 ± 20 ( 55 ± 20 )  MUNE144 ± 117 (272 ± 124)  MUP p-p648 ± 344 (340 ± 80)  Duration ms14.7 ± 5.1  Phases2.6 ± 0.4

68. CMT-X ► Hypothenar Controls  CMAP NP amp4.3 ± 2.3 (>5)  S-MUP NP amp134 ± 30 (80 ± 30)  MUNE32 ± 32 (125 ± 40)  MUP p-p1510 ± 947 (600 ± 50)  Duration ms12.7 ± 2.3  Phases2.8 ± 0.4

69. Hypothenar 15/24 female

72. Normal Probability of finding MUP in a muscle of type: Myopathic Neuropathic

73. Myopathic 25% Normal Probability of finding MUP in a muscle of type: Myopathic Neuropathic

74. Myopathic 50% Normal Probability of finding MUP in a muscle of type: Myopathic Neuropathic

75. Myopathic 75% Normal Probability of finding MUP in a muscle of type: Myopathic Neuropathic

76. Neuropathic 25% Normal Probability of finding MUP in a muscle of type: Myopathic Neuropathic

77. Neuropathic 75% Normal Probability of finding MUP in a muscle of type: Myopathic Neuropathic

78. Summary ► D-QEMG is a robust, valid and reliable method ► Further work is needed to determine the “usefulness” of these tools  Intra-rater reliability across centers  Responsiveness to change  Ease of use in clinical trial setting  Ability to provide evidence of disease presence or progression earlier, or with greater specificity ► ALS ► Myopathies ► Entrapments  Application to measures of NMJ stability

79. Acknowledgements ► Dr. Charles Rice, Dr. Bill Brown ► Shaun Boe, Chris McNeil, Lou Pino ► NSERC ► CIHR ► Dr. Doherty acknowledges the support of the Canada Research Chairs Program

80. Thank-you