emg decomposition
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EMG SIGNAL DECOMPOSITIONUSING PATTERN CLASSIFICATION
TECHNIQUES
Under the guidance ofDr. VINOD
KUMARPROFESSOR &HEAD
By
niranjana rao kakarla
M.Tech.(M&I) 08528010 I.I.T. Roorkee
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Topics to be discussed:-
EMG signal composition
Steps involved in decomposition
Single classifier approachesMultiple classifier approaches
Comparative study
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EMG SIGNAL COMPOSITION Electromyography is the detection of muscle activity
associated with muscle contraction
MOTOR UNIT: a motor unit is an -motoneuron and all themuscle fibres innervated by its axon
MUP waveform: the summation of the spatially andtemporally dispersed potentials, created by impulses
propagating along the individual muscle fibers of a motorunit
MUPT: is the collection of MUP waveforms generated by onemotor unit separated by their inter-discharge intervals
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Fig. 1. MUPT with MUPs separated by their inter-dischargeintervals[1]
The superposition of the MUPTs of all recruited motor unitsand background noise comprises EMG signal
Fig. 2. A 1-s epoch of raw EMG signal[2]
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E M G S IG N A L D E C O M P O S IT IO Nprocess of resolving a composite EMG signal in to its
constituent MUPTs
Figure.3. Schematic representation of the detection anddecomposition of the intramuscular EMG signal[3].
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Applications of decompositionThe shapes and occurrence times of MUPs provide an
important source of information to assist in the diagnosis of
neuromuscular disorders.
reflect the structural and physiological changes of a MU
central nervous system recruitment and control of MUs can
be
studied.
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Major steps involved in EMG signal decomposition are
1. segmentation
2. feature extraction
3. clustering
4. supervised classification
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Segmentation of the composite signal
Segmentation refers to the detection of all MUAPsgenerated by MUs active during signal acquisition.
Fig. 4. (Top) Raw EMG segment recorded from biceps brachii using
concentric needle electrode-a typical "mixed interference pattern" in which
the individual MUAP's are difficult to identify and characterize because of
superimpositions. (bottom) Same segment after filtering using the second-
order differentiating filter[4].
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Fe a tu re e xtra ctio nFeature
The detected MUAPs must be represented using a vector for
pattern recognition.
The characteristics of the MUAPs used for this representation
are called features.
Feature spaces
The multi-dimensional space composed of all possible feature
values is called the feature space.
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feature spaces used to represent MUAPs for signal
decomposition are:
1.peak to peak voltage, number of phases, duration, number of
turns, etc.
2.Fourier transformation coefficients
3.Wavelet coefficients
4.time samples of the band-pass filtered signal
5.time samples of the low-pass differentiated signal.
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CLUSTERING OF DETECTED MUAPs
partitioning detected MUAPs into a number of groups orclusters.
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SINGLE CLASSIFIER APPROACHESCERTAINITY CLASSIFIER
It is a non-parametric template matching classifier
Uses certainty based approach for assigning MUPs to MUPTs
For a set ofM MUPT class labels = {1,2,..,M} the decision
functions for the assignment of MUP mj are evaluated for only
the two MUPTs with the most similar templates to MUP mj
It determines two types of decision functions
1. based on shape information
2. based on firing pattern information
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The shape information decision functions include:
1 .Normalized shape certainty CND : represents the distance
from a candidate MUP mjto the template of a MUPT i1.
2.Relative shape certainty CRD :represents the distance from a
candidate MUP mjto the template of the closest MUPT relative to
the distance from the same MUP to the second closest MUPT.
1.
1.
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The firing pattern information is presented by the firing certainty
based function CFC with respect to the established firing pattern of
the MUPT
The decision of assigning a MUP mjto MUPT i is based on the value
of overall certainty
where i=1,2.
MUPT mj is assigned to MUPT i if overall certainty is the
greatest and if it is greater than the minimal certainty threshold
(Cm) for which a classification is to be made.
Other wise MUP mj is left unassigned.
j
iC
. . j j j ji NDi RDi FCiC C C C =
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FUZZY k-NN CLASSIFIER The fuzzy k-NN classifier uses a fuzzy non-parametric classification
procedure based on the nearest neighbor classification rule.
By passes probability estimation and goes directly to decision
functions.
It determines for each candidate MUP mj a MUPT i membershipi
(mj) representing the shape based strength of membership of mj
in MUPT class i
Also determines firing assertion decision function assesingthe time
of occurrence of MUPT mj with respect to the established firing
pattern of MUPT class i
j
FAiA
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The overall assertion value for assigning MUP mj to MUPT class
i is defined as:
MUP mj is assigned to the MUPT class i with the highest assertion
value and if this value is above the minimum assertion value
threshold (Am) of the MUPT i to which a classification is to be
made.
Otherwise MUP mjis left unassigned.
j
iA
( ).i
j ji j FAi A m A=
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MATCHED TEMPLATE FILTER CLASSIFIERThis method uses correlation measure as an estimate of the degree
of similarity between MUP and MUPT templates.
Two matched template filters are used for supervised MUP
classification
1. normalized cross correlation
2. pseudo- correlation
The MTF classifier also determines for MUP mja firing time similarity
decision function with respect to the established firing pattern
of the MUPT
jFSi
S
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The decision of assigning a MUP to a MUPT is based on the overall
similarity function
Where
MUPT mj is assigned to MUPT iif the value of is greatest and it is
greater than minimal similarity threshold (sm) for which a
classification is to be made.
Otherwise MUP mjis left unassigned.
( ).i
j j j
i FSiS x S
=
j
iS
j
iS
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Multiple Classifier Approaches
Fig.6.Classifier fusion system basicarchitecture[6]
Classifier fusion system architecture belongs to theparallel category of combining classifiers.
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The decision aggregation module in a classifier fusion system
combines the base classifier outputs to achieve a group
consensus.
Majority Voting Aggregation
A MUP x is classified to belong to MUPT class i if over half ofthe classifiers say x i .
Average rule aggregation
Combines the set of decision confidences
1
( )
( )
K
ik
k
i
Cf x
Q xK
==
1( ) argmax ( ( ))M
i i x Q x= =
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One-Stage Classifier Fusion Does not contain the ensemble members selection module
Uses a fixed set of base classifiers
Choosing base classifiers can be performed directly through
exhaustive search with the performance of the fusion being
the objective function.
As the number of base classifiers increases, this approach
becomes computationally too expensive.
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Diversity-Based One-Stage Classifier Fusion Contains an ensemble members selection module
The ensemble choice module selects the subsets of classifiers
that can be combined to achieve better accuracy
The kappa statistic is used to select base classifiers having an
excellent level of agreement to form ensembles having
satisfactory classification performance.
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Hybrid Classifier Fusion Does not contain the ensemble members selection module
Uses a fixed set of base classifiers
It uses a hybrid aggregation module which is a combination of
two stages of aggregation
The first aggregator is based on the abstract level and the
second is based on the measurement level
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The hybrid aggregation scheme works as follows:
First stage:
The outputs of the ensemble of classifiers are presented to
the majority voting aggregator.
If all classifiers state a decision that a MUP pattern is left
unassigned, then it stays unassigned.
If over half of the classifiers assign a MUP pattern to the sameMUPT class, then that MUP pattern is allocated to that MUPT
class and no further assignment is processed.
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Second stage:
This stage is activated for those MUP patterns for which only
half or less than half of the ensemble of classifiers in the
first stage specify a decision for a MUP pattern to be
assigned to the same MUPT class.
The outputs of the ensemble of classifiers are presented to
the average rule aggregator, or the trainable aggregator
represented by the Sugeno fuzzy integral.
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For each MUP pattern, the overall combined confidence
values representing the degree of membership in eachMUPT class are determined
MUP pattern is assigned to the MUPT class for which its
determined overall combined confidence is the largest and
if it is above the specified aggregator confidence threshold
set for that MUPT class
otherwise the MUP pattern is left unassigned
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Diversity-Based Hybrid Classifier FusionThe diversity-based hybrid classifier fusion scheme is a two-
stage process
consists of two aggregators with a pre-stage classifier
selection module for each aggregator.
The ensemble candidate classifiers selected for aggregationare decided through assessing the degree of agreement
using the kappa statistic measure
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The diversity-based hybrid fusion scheme works as follows:
First stage:
classifiers selected for aggregation by the first aggregator are
those having the maximum degree of agreement
The outputs of the classifiers are presented to the majority
voting aggregator.
If all the classifiers state a decision that a MUP pattern is left
unassigned it stays unassigned.
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Second stage:
This stage is used for those MUP patterns for which only half
or less than half of the ensemble classifiers in the first
stage specify a decision for a MUP pattern to be assigned to
the same MUPT class
classifiers selected for aggregation at the second combiner
are those having a minimum degree of agreement
considering only the unassigned category
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The outputs of the classifiers are presented to the average
rule aggregator or the trainable aggregator represented by
Sugeno fuzzy integral aggregator.
For each MUP pattern, the overall combined confidence
values representing the degree of membership in each
MUPT class are determined
MUP pattern is assigned to the MUPT class for which its
determined overall combined confidence is the largest and
if its above the specified aggregator confidence threshold
set for that MUPT class.
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Comparative Study The single classifier and multi-classifier approaches are
compared in terms of the difference between the correctclassification rate CCr% and error rate Er%.
number of MUPs erroneously classified
100number of MUPs assignedrE=
number of MUPs correctly classified100
total number of MUPs detectedr
CC =
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The base classifiers used for experimentation are
1. four ACC classifiers e1,e2, e3, e4
2. four AFNNC classifiers e5,e6,e7,e8
3. four ANCCC classifiers e9,e10 ,e11 ,e12
4. four ApCC classifiers e13 ,e14 ,e15 ,e16
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Classifiers e1, e5, e9, e13 werefed with time-domain
first-order discrete derivative features
Classifiers e2, e6, e10 , e14 were fed withtime-
domain first-order discrete derivative features
Classifiers e3, e7, e11 , e15 werefedwith wavelet-
domain first order discrete derivative features
Classifiers e4, e8, e12 , e16 were fed with wavelet-
domain first-order discretederivative features
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Table .1 Mean and mean absolute deviation (MAD) of the difference betweencorrect classification rate CCr and error rate Er for the different single classifier
approaches across the three EMG signal data sets[9]
C la ssifie r In d e p e n d e n t
sim u la te d sig n a lsR e la te d
sim u la te d sig n a lsR ea lsig n als
e 1 . ( . )8 1 9 4 9 . ( . )7 5 0 2 5 . ( . )7 8 5 0 9
e 2 . ( . )83 9 4 9 . ( . )76 5 1 8 . ( . )72 0 0 3
e3
. ( . )82 3 4 1 . ( . )75 6 1 6 . ( . )71 9 1 4
e4 . ( . )84 7 4 0 . ( . )76 4 1 5 . ( . )67 1 1 4
e5 . ( . )85 2 2 5 . ( . )73 7 1 0 . ( . )80 9 0 9
Bestsingle classifier e6
. ( . )90 4 1 7 . ( . )80 7 2 3 . ( . )77 5 2 6
e7 . ( . )83 1 2 4 . ( . )73 3 0 5 . ( . )73 5 0 4
e8 . ( . )88 9 1 5 . ( . )79 0 1 8 . ( . )73 4 2 4
Average of 8 singleclassifiers
. ( . )85 0 3 3 . ( . )76 2 1 6 . ( . )74 3 1 2
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Table .1 continues..e 9 . ( . )79 3 3 2 . ( . )59 2 0 4 . ( . )69 0 1 3
e10 . ( . )80 6 3 1 . ( . )54 6 2 6 . ( . )63 0 0 7
e11 . ( . )76 1 2 7 . ( . )59 4 1 0 . ( . )58 7 0 4
e12 . ( . )77 7 2 7 . ( . )56 0 0 8 . ( . )49 5 0 3
e13 . ( . )78 0 3 4 . ( . )62 6 0 7 . ( . )71 3 0 4
e14 . ( . )77 8 2 8 . ( . )59 4 0 9 . ( . )66 1 2 7
e15 . ( . )77 5 2 9 . ( . )62 2 0 1 . ( . )68 0 1 5
e16 . ( . )77 6 2 2 . ( . )58 9 0 9 . ( . )56 6 3 6
Average of 16 singleclassifiers
. ( . )81 5 0 1 . ( . )67 7 0 4 . ( . )68 5 0 0
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Table .2 Mean and mean absolute deviation (MAD) of the difference between correctclassification rate CCr and error rate Er for the different single classifier and multi-classifier
approaches across the three EMG signal data sets
cla ssifie r In de p en de nt sim u latedsig n a ls
R e lated
sim u la te d sig n a lsR e al sig n als
S in g le cla ssifie rs
W ea ke st of 8 Sin gleC la ssifie rs e 4
. ( . )84 7 4 0 . ( . )76 4 1 5 . ( . )67 1 1 4
Weakest of 16 Single
Classifier e12. ( . )77 7 2 7 . ( . )56 0 0 8 . ( . )49 5 0 3
Best Single Classifier e6 . ( . )90 4 1 7 . ( . )80 7 2 3 . ( . )77 5 2 6
Average of 8 SingleClassifiers
. ( . )85 0 3 3 . ( . )76 2 1 6 . ( . )74 3 1 2
Average of 16 SingleClassifier
. ( . )81 5 0 1 . ( . )67 7 0 4 . ( . )68 5 0 0
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Table .2 continues
-O n e S ta g e C la ssifie r Fu sio n [ ]2
(Majority Voting fixed of)8
. ( . )86 0 4 6 . ( . )79 2 3 0 . ( . )77 3 4 3
(Average Fixed Rule fixed)of 8
. ( . )88 0 2 5 . ( . )82 0 0 7 . ( . )85 1 1 2
Sugeno Fuzzy Integral
( )fixed of 8
. ( . )82 3 2 7 . ( . )78 3 1 9 . ( . )80 9 2 9
- - [ ]Diversity based One Stage Classifier Fusion 2
/Majority Voting 6 8 . ( . )87 6 4 2 . ( . )80 1 2 7 . ( . )78 8 4 8
/Average Fixed Rule 6 8 . ( . )88 5 2 2 . ( . )82 1 1 1 . ( . )84 9 0 8
/Sugeno Fuzzy Integral 6 8 . ( . )84 6 2 4 . ( . )80 2 1 1 . ( . )82 0 0 8
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AMVAFR, ADMVAFR stands for Adaptive (or Diversity-based) Majority Voting
with Average Fixed Rule hybrid classifier fusion scheme, respectively.AMVSFI, ADMVSFI stands for Adaptive (or Diversity-based) Majority Voting withSugeno Fuzzy Integral one-stage classifier fusion scheme, respectively.
[ ]H y b rid C la ssifie r Fu sio n 7
( )A M V A F R fix e d o f 6 . ( . )9 1 8 1 8 . ( . )8 4 6 1 3 . ( . )8 2 7 2 5
( )A M VS FI fixed of 6 . ( . )9 1 8 1 8 . ( . )8 4 6 1 3 . ( . )8 2 5 1 7
- [ ]D ive rsity b a se d H yb rid C la ssifie r Fu sio n 6
/A D M V A FR 6 8 . ( . )9 1 6 1 8 . ( . )8 4 4 0 7 . ( . )8 5 5 0 9
/A D M V S F I 6 8 . ( . )9 1 2 1 8 . ( . )8 4 0 0 8 . ( . )8 5 2 0 9
/A D M V A FR 6 1 6 . ( . )9 0 0 3 2 . ( . )8 3 2 0 7 . ( . )8 3 7 0 6
/A D M V S F I 6 1 6 . ( . )8 9 6 3 3 . ( . )8 2 5 0 8 . ( . )8 2 8 0 4
Table .2 continues..
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Conclusion one-stage aggregator classifier fusion and its diversity-based
variant schemes have performance better than the average
performance of the base classifiers
Better than the performance of the best base classifier except
across the independent simulated signals.
The hybrid classifier fusion and its diversity-based variant
approaches have performances that not only exceed the
performance of any of the base classifiers forming the
ensemble but also reduced classification errors for all data
sets studied
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References1.Rasheed S, Stashuk D and Kamel M (2008) Fusion of multiple
classifiers for motor unit potential sorting. BiomedicalSignal Processing and Control, 3(3):229243
2.Rasheed S, Stashuk D and Kamel M (2008) Diversity-based
combination of non-parametric classifiers for EMG signaldecomposition. Pattern Analysis & Applications, 11:385408
3.Stashuk D W (2001) EMG signal decomposition: how can it beaccomplished and used? Journal of
Electromyography and Kinesiology, 11:1511734.McGill K C, Cummins K and Dorfman L J (1985) Automatic
decomposition of the clinical electromyogram. IEEETransactions on Biomedical Engineering, 32(7):470477
5.
6.
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5. Nikolic M, Srensen J A, Dahl K et al. (1997) Detailed analysis ofmotor unit activity. In Proceedings of the 19th Annual
International Conference of the IEEE Engineering in Medicineand Biology Society Conference, 12571260
6.Sarbast Rasheed, Daniel W. Stashuk, Mohamed S.Kamel(2002) Integrating Heterogeneous ClassifierEnsembles for EMG Signal Decomposition Based onClassifier Agreement IEEE transactions on informationtechnology in biomedicine, 1(11):1-17
7. Rasheed S, Stashuk D and Kamel M(2007) A hybrid classifierfusion approach for motor unit potential classification duringEMG signal decomposition. IEEE Transactions on BiomedicalEngineering, 54(9):17151721
8. Roberto Merletti, Philip Parker (2004) Electromyography:Physiology, Engineering, and Non-Invasive Applications.Wiley-IEEE Press, 1st edition
9. Amine Nait-ali (2009) Advanced Biosignal Processing . Springer,1st edition
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