emg decomposition

8/6/2019 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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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

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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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