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DQEMG: Decomposition-based Quantitative EMG Daniel W. Stashuk, PhD University of Waterloo Timothy J....
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Transcript of DQEMG: Decomposition-based Quantitative EMG Daniel W. Stashuk, PhD University of Waterloo Timothy J....
DQEMG: DQEMG: Decomposition-based Quantitative Decomposition-based Quantitative
EMGEMG
Daniel W. Stashuk, PhDDaniel W. Stashuk, PhDUniversity of WaterlooUniversity of Waterloo
Timothy J. Doherty MD, PhD, FRCPCTimothy J. Doherty MD, PhD, FRCPCThe University of Western OntarioThe 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/.
ObjectivesObjectives
► Brief review of quantitative EMGBrief review of quantitative EMG► Principles of decomposition EMGPrinciples of decomposition EMG► Basic overview of DQEMGBasic overview of DQEMG► How can DQEMG be used for clinical and How can DQEMG be used for clinical and
research applicationsresearch applications► Validation and reliability of the methodValidation and reliability of the method
Important Neuromuscular Important Neuromuscular InformationInformation
Numbers of MUs Relative sizes of MUs Morphology of MUs Recruitment of MUs Firing patterns of MUs Functional stability of neuromuscular junctions
Why Develop Quantitative EMG Methods?
• Objectivity
• Increased Sensitivity
• Increased Specificity
• Ability to determine degree of involvement
• Improved ability provide longitudinal
assessments
Quantitative EMG methodsQuantitative EMG methods
►Single Fiber EMGSingle Fiber EMG►Quantitative MUP analysisQuantitative MUP analysis
Semi-automated MUP analysisSemi-automated MUP analysis Automated MUP analysisAutomated MUP analysis
► Interference pattern analysisInterference pattern analysis►Motor unit number estimationMotor unit number estimation
Quantitative EMG methodsQuantitative EMG methods
►Single Fiber EMGSingle Fiber EMG►Quantitative MUP analysisQuantitative MUP analysis
Semi-automated MUP analysisSemi-automated MUP analysis Automated MUP analysisAutomated MUP analysis
► Interference pattern analysisInterference pattern analysis►Motor unit number estimationMotor unit number estimation
What is an EMG Signal?What is an EMG Signal?
MFPMFP - muscle fibre potential - muscle fibre potential
MUPMUP - motor unit potential - motor unit potential
MUPT - motor unit potential trainMUPT - motor unit potential train
Composite EMGComposite EMG
EMG Signal CompositionEMG Signal Composition
MUPT MUPT - components of a MUPT- components of a MUPT
kN
1ikikik -tMUPtMUPT
What is an Electromyographic (EMG) What is an Electromyographic (EMG) Signal?Signal?
)t(t)t(1
nMUPTEMGmN
mm
Example Composite EMG SignalExample Composite EMG Signal
Concepts of EMG Signal Concepts of EMG Signal DecompositionDecomposition
Basic AssumptionsBasic Assumptions:: Each MUP can be detectedEach MUP can be detected Each detected MUP can be recognizedEach detected MUP can be recognized
Basic Requirements: Common MUP feature is available for detectionCommon MUP feature is available for detection
MUPs within the same MUPT are more similar MUPs within the same MUPT are more similar
than MUPs from different MUPTsthan MUPs from different MUPTs
Typical MUPs can be determined for each MUPT Typical MUPs can be determined for each MUPT
(i.e., MUPs must occur in isolation)(i.e., MUPs must occur in isolation)
How can an EMG Signal Be Decomposed?
Steps in EMG Signal DecompositionSteps in EMG Signal Decomposition
Signal AcquisitionSignal Acquisition
Segmentation (Detecting MUPs)Segmentation (Detecting MUPs)
Feature ExtractionFeature Extraction
Clustering of Detected MUPsClustering of Detected MUPs
Supervised Classification of Detected MUPsSupervised Classification of Detected MUPs
Discovering MUPT Temporal RelationshipsDiscovering MUPT Temporal Relationships
Resolving Superimposed MUPsResolving Superimposed MUPs
Steps In Signal DecompositionSteps In Signal Decomposition
Steps In Signal DecompositionSteps In Signal Decomposition
Steps In Signal DecompositionSteps In Signal Decomposition
DQEMGDQEMG► Performs multiple passes through an EMG signal to complete a Performs multiple passes through an EMG signal to complete a
partialpartial decomposition decomposition► Detection passDetection pass
absolute or relative criteriaabsolute or relative criteria multiply and disparately detected MUPsmultiply and disparately detected MUPs
► Clustering passClustering pass STBC (shape and temporal based clustering)STBC (shape and temporal based clustering) Analyzes a selected portion of the signal (3 to 8 s long)Analyzes a selected portion of the signal (3 to 8 s long)
► Multiple Supervised Classification PassesMultiple Supervised Classification Passes Certainty-based classificationCertainty-based classification Analyzes complete signalAnalyzes complete signal Robustly and actively uses firing pattern information Robustly and actively uses firing pattern information
► Temporal relationships passTemporal relationships pass accounts foraccounts for multiply (linked MUPTs)multiply (linked MUPTs)
and disparately (exclusive MUPTs) detected MUPsand disparately (exclusive MUPTs) detected MUPs► Superimposed MUPs are Superimposed MUPs are notnot resolved resolved
DQEMGDQEMG► Concentric or Concentric or
mono-polar needle and mono-polar needle and surface or “macro” surface or “macro” EMG signals acquiredEMG signals acquired
► 30 - 60 sec 30 - 60 sec isometric contractionsisometric contractions
► Mild to moderate intensityMild to moderate intensity
► Twenty or more MUs Twenty or more MUs from 4 – 6 contractionsfrom 4 – 6 contractions
Acquiring EMG SignalsAcquiring EMG Signals
► Apply surface electrode configuration as per Apply surface electrode configuration as per standard motor study with active electrode standard motor study with active electrode over motor point.over motor point.
► Acquire maximal CMAPAcquire maximal CMAP
Acquiring EMG SignalsAcquiring EMG Signals
Acquiring EMG SignalsAcquiring EMG Signals► Apply surface electrode configuration as Apply surface electrode configuration as
per standard motor study with active per standard motor study with active electrode over motor point.electrode over motor point.
► Acquire maximal CMAPAcquire maximal CMAP
► Perform MVC and calculate MVCRMSPerform MVC and calculate MVCRMS
Acquiring EMG SignalsAcquiring EMG Signals
Acquiring EMG SignalsAcquiring EMG Signals► Apply surface electrode configuration as per Apply surface electrode configuration as per
standard motor study with active electrode over standard motor study with active electrode over motor point.motor point.
► Acquire maximal CMAPAcquire maximal CMAP► Perform MVC and calculate MVCRMSPerform MVC and calculate MVCRMS
► Insert needle electrode and position for sufficient Insert needle electrode and position for sufficient signal quality (signal quality monitor V/s or kV/ssignal quality (signal quality monitor V/s or kV/s22).).
► Have subject isometrically contract to desired Have subject isometrically contract to desired level of effort or signal intensity level of effort or signal intensity (%MVCRMS effort and pps intensity monitors) (%MVCRMS effort and pps intensity monitors)
Acquiring EMG SignalsAcquiring EMG Signals
Acquiring EMG SignalsAcquiring EMG Signals► Apply surface electrode configuration as per Apply surface electrode configuration as per
standard motor study with active electrode over motor standard motor study with active electrode over motor point.point.
► Acquire maximal CMAPAcquire maximal CMAP► Perform MVC and calculate MVCRMSPerform MVC and calculate MVCRMS► Insert needle electrode and position for sufficient Insert needle electrode and position for sufficient
signal quality (signal quality monitor V/s or kV/ssignal quality (signal quality monitor V/s or kV/s22). ). ► Have subject isometrically contract to desired Have subject isometrically contract to desired
level of effort or signal intensity level of effort or signal intensity (%MVCRMS effort and pps intensity monitors) (%MVCRMS effort and pps intensity monitors)
► Decompose needle acquired signal Decompose needle acquired signal and calculate available SMUPs and calculate available SMUPs
► Reposition needle and repeat during a Reposition needle and repeat during a subsequent contractionsubsequent contraction
► Continue until sufficient number of SMUPs acquired (20 –35)Continue until sufficient number of SMUPs acquired (20 –35)
Acquiring EMG SignalsAcquiring EMG Signals
Quantitative MUP analysisQuantitative MUP analysis
► Buchthal method – 1950’sBuchthal method – 1950’s► Concentic needle MUPs collected one-by-one Concentic needle MUPs collected one-by-one
from minimally contracting musclefrom minimally contracting muscle SlowSlow ++ patient and operator interaction++ patient and operator interaction Biased population of MUPsBiased population of MUPs Relatively limited information provided – Relatively limited information provided –
only MUP parametersonly MUP parameters
Quantitative Needle EMGQuantitative Needle EMGMorphological parameters - prototypical MUP of each MUPT
duration, number of phases, turns, Vpp, area, area/amplitude ratio
Spike-triggered AveragingSpike-triggered Averaging
Motor Unit Number Motor Unit Number EstimationEstimation
Size of M-potential
Mean S-MUP Size
= MUNE
McComas et al. 1971
Mean S-MUP template MUNE
CMAP
Measurement of MUP Stability
Jitter Measurement
Jitter MCD: 17.9 SBlocking: 0%Mean IPI: 219 S
Methods ofMethods ofacquiring sampleacquiring sample
of S-MUPsof S-MUPs
IncrementalStimulation
MPS STA
Statistical
Quantitative Needle EMGQuantitative Needle EMG
Decomposition-Based S-MUPsDecomposition-Based S-MUPs
Decomposition-Based S-MUPsDecomposition-Based S-MUPs
Decomposition-based mS-MUAPDecomposition-based mS-MUAP
Size PrincipleSize Principle
MUs with smallertwitch tensions andslower contraction timesare recruited beforelarger, faster MUs
Will this impact the sizesof MUPs collected withD-QEMG?
Effect of force onEffect of force on needle detected MUP and needle detected MUP and surface MUP sizesurface MUP size
Boe et al. 2005 Muscle and Nerve
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 50 100 150 200 250 300
Force (N)
RM
S (
mV
)
0
0.25
0.5
0.75
1
1.25
1.5
0 20 40 60 80 100
Force (N)
RM
S (
mV
)
0
0.5
1
1.5
2
2.5
3
0 100 200 300 400 500
Force (N)
RM
S (
mV
)
0
0.5
1
1.5
2
2.5
3
0 50 100 150 200 250 300 350 400
Force (N)
RM
S (
mV
)
Biceps Force-EMG RelationshipsBiceps 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)
Force-EMG RelationshipsForce-EMG Relationships
Pearson Correlation ( r )Pearson Correlation ( r )
SubjectSubject BicepsBiceps FDIFDI
11 0.98620.9862 0.93050.9305
22 0.99240.9924 0.96210.9621
33 0.99200.9920 0.77930.7793
44 0.97290.9729 0.94180.9418
55 0.97190.9719 0.98470.9847
66 0.98050.9805 0.73320.7332
77 0.98590.9859 0.74840.7484
88 0.95540.9554 0.98450.9845
99 0.96280.9628 0.98640.9864
1010 0.96630.9663 0.89420.8942
MinimumMinimum 0.95540.9554 0.73320.7332
MaximumMaximum 0.99240.9924 0.98470.9847
MeanMean 0.97660.9766 0.89450.8945
Application of DQEMGApplication of DQEMGto the study of Agingto the study of Aging
McNeil, Doherty, Stashuk, Rice (2005) Motor unit number McNeil, Doherty, Stashuk, Rice (2005) Motor unit number estimates in the tibialis anterior muscle of young, old and very estimates in the tibialis anterior muscle of young, old and very old men. old men. Muscle and NerveMuscle and Nerve. 31: 461-67. 31: 461-67
► Does progressive MU Does progressive MU loss contribute to loss of loss contribute to loss of strength in the very old?strength in the very old?
► Measured strength, Measured strength, muscle mass, MUNEs in muscle mass, MUNEs in the tibialis anterior the tibialis anterior muscle of three groups muscle of three groups of menof men
► Age GroupsAge Groups Young 27 Young 27 3 yrs 3 yrs Old 66 Old 66 3 yrs 3 yrs Very Old 82 Very Old 82 4 yrs 4 yrs
012345678
Young Old Very Old
Age Group
Nega
tive
Peak
Am
plitu
de (m
V)
0
20
40
60
80
100
Young Old Very Old
Age Group
Nega
tive
Peak
Am
plitu
de (µ
V)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
*
*
020406080
100120140160180
Young Old Very Old
Age Group
Num
ber o
f Mot
or U
nits
TA MUNEsTA MUNEs
Loss of muscleMass and StrengthFunctional Decline
*
**
DQEMG in CMTXDQEMG in CMTX
► CMTX is the X-linked form of hereditary motor-sensory CMTX is the X-linked form of hereditary motor-sensory neuropathy – 2neuropathy – 2ndnd most common following CMT1A most common following CMT1A
► Numerous mutations of the GJNumerous mutations of the GJββ1 gene leading to 1 gene leading to abnormalities in Connexin-32 proteinabnormalities in Connexin-32 protein
► Distal wasting and weakness by 3Distal wasting and weakness by 3rdrd decade in males – decade in males – females usually milder coursefemales usually milder course
► Conduction slowing in intermediate range (30 - 40 Conduction slowing in intermediate range (30 - 40 m/s)m/s)
► Reduced motor and sensory amplitudesReduced motor and sensory amplitudes► As part of a longitudinal study we have examined the As part of a longitudinal study we have examined the
hypothenar and biceps/brachialis muscles of 58 hypothenar and biceps/brachialis muscles of 58 subjects at baseline with D-QEMGsubjects at baseline with D-QEMG
CMT-XCMT-X
►Biceps/brachialisBiceps/brachialis ControlsControls CMAP NP ampCMAP NP amp 6.6 6.6 ± 3.6± 3.6 ( 12 ± 2)( 12 ± 2)
S-MUP NP ampS-MUP NP amp 45 ± 2045 ± 20 ( 55 ± 20 )( 55 ± 20 )
MUNEMUNE 144 144 ± 117± 117(272 ± 124)(272 ± 124)
MUP p-pMUP p-p 648 ± 344 648 ± 344 (340(340 ± ± 80)80)
Duration msDuration ms 14.7 ± 5.114.7 ± 5.1 PhasesPhases 2.6 ± 0.42.6 ± 0.4
CMT-XCMT-X
►HypothenarHypothenar ControlsControls CMAP NP ampCMAP NP amp 4.3 4.3 ± 2.3± 2.3 (>5)(>5)
S-MUP NP ampS-MUP NP amp 134 ± 30134 ± 30 (80 ± 30)(80 ± 30)
MUNEMUNE 32 32 ± 32± 32 (125 ± (125 ± 40)40)
MUP p-pMUP p-p 1510 ± 9471510 ± 947 (600(600 ± 50)± 50)
Duration msDuration ms 12.7 ± 2.312.7 ± 2.3 PhasesPhases 2.8 ± 0.42.8 ± 0.4
HypothenarHypothenar
0 1 2 3 4 5 6 7 8 9 100
25
50
75
100
125
150
Hypothenar CMAP neg pk
MU
NE
15/24 female
NormalNormal
Probability of finding MUP in a muscle of type:
Myopathic
Neuropathic
Myopathic 25%Normal
Probability of finding MUP in a muscle of type:
Myopathic
Neuropathic
Myopathic 50%Normal
Probability of finding MUP in a muscle of type:
Myopathic
Neuropathic
Myopathic 75%Normal
Probability of finding MUP in a muscle of type:
Myopathic
Neuropathic
Neuropathic 25%Normal
Probability of finding MUP in a muscle of type:
Myopathic
Neuropathic
Neuropathic 75%Normal
Probability of finding MUP in a muscle of type:
Myopathic
Neuropathic
SummarySummary► D-QEMG is a robust, valid and reliable methodD-QEMG is a robust, valid and reliable method► Further work is needed to determine the Further work is needed to determine the
“usefulness” of these tools“usefulness” of these tools Intra-rater reliability across centersIntra-rater reliability across centers Responsiveness to changeResponsiveness to change Ease of use in clinical trial settingEase of use in clinical trial setting Ability to provide evidence of disease presence Ability to provide evidence of disease presence
or progression earlier, or with greater specificityor progression earlier, or with greater specificity► ALSALS► MyopathiesMyopathies► EntrapmentsEntrapments
Application to measures of NMJ stabilityApplication to measures of NMJ stability
AcknowledgementsAcknowledgements
► Dr. Charles Rice, Dr. Bill BrownDr. Charles Rice, Dr. Bill Brown► Shaun Boe, Chris McNeil, Lou PinoShaun Boe, Chris McNeil, Lou Pino► NSERCNSERC► CIHRCIHR► Dr. Doherty acknowledges the support of Dr. Doherty acknowledges the support of
the Canada Research Chairs Programthe Canada Research Chairs Program
Thank-you