Transcript DQEMG: From theory to application

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/.
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
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
Why Develop Quantitative EMG Methods?
• Objectivity
• Increased Sensitivity
• Increased Specificity
• Ability to determine degree of involvement
• Improved ability provide longitudinal assessments
Quantitative EMG methods
► Single
Fiber EMG
► Quantitative MUP analysis
 Semi-automated MUP analysis
 Automated MUP analysis
► Interference
pattern analysis
► Motor unit number estimation
Quantitative EMG methods
► Single
Fiber EMG
► Quantitative MUP analysis
 Semi-automated MUP analysis
 Automated MUP analysis
► Interference
pattern analysis
► Motor unit number estimation
What is an EMG Signal?
 MFP
- muscle fibre potential

MUP
- motor unit potential

MUPT

Composite EMG
- motor unit potential train
EMG Signal Composition
MUPT
- components of a MUPT
Nk
MUPTk t    MUPki t -  ki 
i 1
What is an Electromyographic (EMG) Signal?
Nm
EMG( t )   MUPTm t   n( t )
m1
Example Composite EMG Signal
Concepts of EMG Signal Decomposition
How can an EMG Signal Be Decomposed?
Basic Assumptions:
 Each MUP can be detected
 Each detected MUP can be recognized
Basic Requirements:

Common MUP feature is available for detection

MUPs within the same MUPT are more similar than
MUPs from different MUPTs

Typical MUPs can be determined for each MUPT
(i.e., MUPs must occur in isolation)
Steps in EMG Signal Decomposition
 Signal Acquisition

Segmentation (Detecting MUPs)

Feature Extraction

Clustering of Detected MUPs

Supervised Classification of Detected MUPs

Discovering MUPT Temporal Relationships

Resolving Superimposed MUPs
Steps In Signal Decomposition
Steps In Signal Decomposition
Steps In Signal Decomposition
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
►
►
►
►
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
Acquiring EMG Signals
► Apply
surface electrode configuration as per
standard motor study with active electrode over
motor point.
► Acquire
maximal CMAP
Acquiring EMG Signals
Acquiring EMG Signals
► Apply
surface electrode configuration as per
standard motor study with active electrode over
motor point.
► Acquire
maximal CMAP
► Perform
MVC and calculate MVCRMS
Acquiring EMG Signals
Acquiring EMG Signals
► 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/s2).
► Have
subject isometrically contract to desired
level of effort or signal intensity
(%MVCRMS effort and pps intensity monitors)
Acquiring EMG Signals
Acquiring EMG Signals
►
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/s2).
►
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)
Acquiring EMG Signals
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
Quantitative Needle EMG
Morphological parameters - prototypical MUP of each MUPT

duration, number of phases, turns, Vpp, area, area/amplitude ratio
Spike-triggered Averaging
Motor Unit Number Estimation
Size of M-potential
Mean S-MUP Size
= MUNE
McComas et al. 1971
CMAP
Mean S-MUP template
MUNE
Measurement of MUP Stability
Jitter Measurement
Jitter MCD: 17.9 mS
Blocking: 0%
Mean IPI: 219 mS
Incremental
Stimulation
MPS
Methods of
acquiring sample
of S-MUPs
Statistical
STA
Quantitative Needle EMG
Decomposition-Based S-MUPs
Decomposition-Based S-MUPs
Decomposition-based mS-MUAP
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?
Effect of force on needle detected MUP and
surface MUP size
Boe et al. 2005 Muscle and Nerve
Biceps Force-EMG Relationships
3
0.9
0.8
2.5
RMS (mV)
RMS (mV)
0.7
2
1.5
1
0.6
0.5
0.4
0.3
0.2
0.5
0.1
0
0
100
200
300
400
0
500
0
50
100
150
200
250
300
Force (N)
3
1.5
2.5
1.25
2
1
RMS (mV)
RMS (mV)
Force (N)
1.5
1
0.75
0.5
0.25
0.5
0
0
0
50
100
150
200
250
300
350
Force (N)
Data from 2 control subjects,
r values of 0.99 (top) and
0.99 (bottom)
400
0
20
40
60
80
Force (N)
Data from 2 ALS subjects, r
values of 0.96 (top) and 0.99
(bottom)
100
Force-EMG Relationships
Pearson Correlation ( r )
Subject
Biceps
FDI
1
0.9862
0.9305
2
0.9924
0.9621
3
0.9920
0.7793
4
0.9729
0.9418
5
0.9719
0.9847
6
0.9805
0.7332
7
0.9859
0.7484
8
0.9554
0.9845
9
0.9628
0.9864
10
0.9663
0.8942
Minimum
0.9554
0.7332
Maximum
0.9924
0.9847
Mean
0.9766
0.8945
Application of DQEMG
to the study of Aging
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
Negative Peak Amplitude (mV)
Maximum M
8
7
6
5
4
3
2
1
0
*
MVIC strength
Young
Old
Very Old
Negative Peak Amplitude (µV)
Age Group
100
Mean S-MUP Size
*
80
60
40
20
0
Young
Old
Very Old
Age Group
McNeil et al. 2004 Muscle and Nerve
• Young – 39 Nm
• Old – 38 Nm
• Very old – 30 Nm
Number of Motor Units
TA MUNEs
180
160
140
120
100
80
60
40
20
0
*
**
Young
Old
Very Old
Age Group
Loss of muscle
Mass and Strength
Functional Decline
DQEMG in CMTX
CMTX is the X-linked form of hereditary motor-sensory
neuropathy – 2nd most common following CMT1A
► Numerous mutations of the GJβ1 gene leading to
abnormalities in Connexin-32 protein
► Distal wasting and weakness by 3rd 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
►
CMT-X
► Biceps/brachialis






CMAP NP amp
S-MUP NP amp
MUNE
MUP p-p
Duration ms
Phases
Controls
6.6 ± 3.6
45 ± 20
144 ± 117
648 ± 344
14.7 ± 5.1
2.6 ± 0.4
( 12 ± 2)
( 55 ± 20 )
(272 ± 124)
(340 ± 80)
CMT-X
► Hypothenar






CMAP NP amp
S-MUP NP amp
MUNE
MUP p-p
Duration ms
Phases
Controls
4.3 ± 2.3
134 ± 30
32 ± 32
1510 ± 947
12.7 ± 2.3
2.8 ± 0.4
(>5)
(80 ± 30)
(125 ± 40)
(600 ± 50)
Hypothenar
150
125
MUNE
100
75
50
15/24 female
25
0
0
1
2
3
4
5
6
7
8
Hypothenar CMAP neg pk
9
10
Normal
Normal
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
Normal
Myopathic 75%
Probability of finding
MUP in a muscle of type:
Myopathic
Neuropathic
Normal
Neuropathic 25%
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
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
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
Thank-you