OPTIMIZATION OF EMG-SIGNAL SOURCE CLASSIFICATION

BASED ON ADAPTIVE WAVELETS K-MEAN ALGORITHM

Abbas K. Abbas

, Rasha Bassam

and Rana M. Kasim

1,3

Biomedical Engineering Dept. RWTH Aachen University, Germany

Biomedical Engineering Dept. Aachen University of Applied Sciences, Germany

Keywords: EMG decomposition, Spike overlapping, Wavelet coefficient, MUAP’s clustering, Firing spikes.

Abstract: In this paper the optimization of EMG signals segmentation and decomposition based on wavelet represen-

tation and k-mean clustering technique is presented. It is shown that wavelet decomposition can be usefull

in detecting particular spikes in EMG signals and the presented segmentation algorithm may be useful for

the detection of active segments in related MUAP’s action potentials. The algorithms has been tested on the

synthetic model signal and on real signals recorded with intramuscular multi-point electrode. The efficiency

of EMG signal decomposition and classification with adaptive wavelet algorithm were presented. Single

and multiple fibers MUAP patterns were tested and identified. By applying a Debauchies wavelet transfor-

mation and k-mean clustering algorithm to localize the action-potential source in the presence of specific

neuromuscular diseases like NMI neuropathy, muscular dystrophy and myasthenia gravis (MG), instead of

many decomposition and pattern recognition algorithm, wavelets and k-mean clustering have its flexibility

for robustly classify and localize the signal stochastic sources with a linear way, in addition to identify the

blind source for EMG bioelectric potential.

1 INTRODUCTION

Electromyography (EMG) signals classification and

processing can be used for varieties of clini-

cal/biomedical applications, spectral pattern classifi-

cation of intensity-based analysis, and modern hu-

man computer interaction. EMG signals acquired

from muscles require advanced methods for detec-

tion, decomposition, processing, and classification.

The resolution of a composite EMG signal into its

significant, constituent MUAPTs requires the ability

to detect the discharges (i.e., MUAPs) of the MUs

significantly correlating to the composite signal and

to correctly combine each detected MUAP with the

MU that generated it. EMG signal decomposition

therefore involves the two basic steps of detecting

MUAPs and recognizing detected MUAPs. To iden-

tify the occurrences of consecutive MUAP’s poten-

tial signal a parallel electrode should be placed in the

path of depolarization waveform, for recording such

activities, which considered as a vital point in

MUAP’s acquisition technique (D. Zazula, 1999).

The basic steps of intramuscular EMG signal acqui-

sition was illustrated in fig.1 were the recording

electrode detecting spontaneous electrical activity of

different myofibers on the basis of three electrical

wave propagation zones (1) innervations zone ,(2)

depolarization zone and (3) terminal zone. These

will accumulatively constructing the different pat-

tern of EMG signals. The definition of MUAP’s

potential of this scheme, were the spontaneous elec-

trical activity to be recoded can be observed in real

time synchronous EMG signal recording technique

(Wang et. al, 1997). Adaptive signal decomposition

technique have a principal rule in defining elementa-

ry methods for EMG signals classification and

processing, which can be used for varieties of clini-

cal/biomedical applications, spectral pattern classifi-

cation of intensity-based development, and modern

human computer interaction. The purpose of this

paper is to illustrate the various methodologies and

algorithms for EMG signal pattern classification

based on wavelet signal decomposition to provide

efficient and effective ways of understanding the

signal and its physiological nature.

2 MATERIALS AND METHODS

EMG signal recorded using Delsys

system for

recording surface (sEMG) and needle EMG with

sensitivity between (0.2-10 uV). The suspected area

of disorder is identified for EMG recording, for

example, the biceps brachii in the upper arm. The

491

K. Abbas A., Bassam R. and M. Kasim R. (2009).

OPTIMIZATION OF EMG-SIGNAL SOURCE CLASSIFICATION BASED ON ADAPTIVE WAVELETS K-MEAN ALGORITHM.

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pages 491-497

DOI: 10.5220/0001542804910497

 SciTePress

EMG is then triggered to record for a predetermined

time after which the acquired signal is differentially

amplified, band pass filtered, and digitized. The

common feature for classifying intramuscular EMG

signal is the Euclidean distance between the MUAP

waveforms. For clinical interests, the main feature of

EMG signal is the number of active motor unit

(MUs), the MUAP waveforms, and the innervations

time statistics. According to De Luca method (D.

Zazula, 1999), the determination of the MUAP

waveform and the number of active MUs can be

considered as a classification problem, and for fur-

ther analysis of EMG signals

(Wang et. al, 1997;

Thompson et. al, 1996).

Figure 1: Intramuscular EMG signal acquisition with

typical MUAP action potential (Wang et. al, 1997).

The representation of time-triggered and no overlap-

ping MUAPs produce a shimmer. MUAP shimmer

is influenced by the time-offset of the sampled

waveforms, local fluctuation of the baseline and

background noise. Besides background noise and the

effects of signal offset, white noise influences the

classification. The classification with wavelet coef-

ficient needs the wavelet coefficient (Ff[m,n]) of

four frequency bands (m=2, 3, 4, 5) and not below

150 Hz. Classification performance depends also on

distance between the class means, therefore, the best

selection of these four frequency bands depends on

the Fourier transform of the MUAP waveforms

themselves. Boualem (Wang et. al, 1997) theorized

that the time frequency representation of wavelets

decomposition (WVD) provided high-resolution

signal characterization in time-frequency space and

good noise rejection performance as fig.2 illustrated

the continuous wavelet transformation for the EMG

signals using a Db-WT. This theory is useful for

EMG signal classification. For purpose of classify-

ing EMG patterns, EMG electrical model is used in

combination with wavelet decomposition by ex-

tracted and compared two types of features based on

signal processing for the purpose of classifying

EMG patterns. The two features were the coeffi-

cients of EMG signal the components of Fourier

frequency spectra. The method showed better results

while describing the EMG linear envelopes (LE)

method (McKeown et. al, 2002).

Figure 2: EMG wavelets transformation for single MUAP

using Db WT (McKeown et. al, 2002).

3 EMG PROCESSING METHOD

The complexity of a detected EMG signal and the

ease with which it can be decomposed depend on the

type of electrode, electrode positioning, profile of

muscle contraction, and muscle selected. The elec-

trode should be positioned so that it is close to active

muscle fibers and detects MUAPs of maximum

amplitude and sharpness in order to maximize the

relative differences in the distances between the

fibers of different MUs and the electrode surface.

MUAPs distinct from the background noise can be

detected in this way. The suggested procedure is to

initially position the electrode in a minimally con-

tracting muscle to detect MUAPs of maximum am-

plitude and sharpness, and then to increase muscle

contraction as isometrically as possible and initiate

data acquisition once the contraction is at the desired

level. If the decomposition system can process sig-

nals acquired during force-changing contractions,

data acquisition should start immediately after

needle positioning (McKeown et. al, 2002; M. J.

McKeown, 2002).

4 MUAPS VOLTAGE

DETECTION

AND RESOLUTION

Complete EMG signal decomposition requires the

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detection of all MUAPs generated by MUs active

during signal acquisition. In practice, however, there

are many MUAPs produced by MUs with no fibers

close to the detection surface. These MUAPs are

generally small, primarily of low-frequency content,

and similarly shaped. Therefore, it is difficult to

consistently assigning such MUAPs to their correct

MUAP and it is easy to miss the small MUAPs

when they occur in close temporal proximity to

larger MUAPs. Consequently, it is more useful to

only detect MUAPs that can be consistently correct-

ly assigned. MUAP detection usually involves calcu-

lating, for each sample of the composite signal ac-

quired, a statistic and comparing its value to a pre-

set threshold. Some of the signal statistics used in-

clude the raw or band pass-filtered signal amplitude

(M. J. McKeown, 2002; Fang et. al, 1999) or va-

riance (Thompson et. al, 1996; McKeown et. al,

2002), or a combination of both raw signal slope and

amplitude (Thompson et. al, 1996; Jung et. al, 2001).

When the threshold value is exceeded, a candidate

MUAP can be defined as a fixed length section of a

neighboring signal or a variable-length signal sec-

tion, assumed to possibly contain several significant

MUAP contributions (Wang et. al, 1997; Thompson

et. al, 1996). Any selected signal section may be an

isolated MUAP, a superposition of MUAPs from

two or more MUs, only a portion of a single MUAP,

or a spurious noise spike. Therefore, before further

processing, it is required that the composition of a

detected section be determined, and properly aligned

and represented.

0 5 10 15 20 25 30 35 40 45

Time msec

MUAP Amplitude uV

muap5

muap4

muap3

muap2

muap1

Maximum spike

amplitude of

MUAP

Figure 3: Localization of MUAP’s intensity profile within

repeated recording of single needle EMG electrode (Fang

et. al, 1999).

5 WAVELETS

TRANSFORMATION

The wavelet transform (WT) of signal S(t) corre

sponds to its decomposition with respect to a family

of function obtained by dilations and translations of

an analyzing wavelet denoted V(t). The coefficients

WS (a,b) deduced from this decomposition are ex-

pressed by:

dttstsbaWS

baba

)()(,),(

∫

∞

∞−

∗

ψψ

(1)

where the superscript

* denotes the complex conju-

gate. The parameter a and b are the scale factor and

shift factor respectively. This transformation acts on

the signal as a filter bank whose frequency charac-

teristics are linked to

ψ(t) and to the parameter U. In

multiresolution signal analysis (Thompson et. al,

1996),

WT may be used to decompose a signal at

various resolutions. The details of a signal at differ-

ent resolutions generally characterize different

physical structures. From wavelet representation, the

exact reconstruction of the signal can be carried out.

This makes it feasible to compute and manipulate

data in compressed parameters via WT.

These parameters characterize the behaviour of the

signal and can be served as features. In our paper,

we select wavelet coefficient with the maximum

absolute value at each scale to be the features of

EMG signals. These values represent in some way

the correlation between the raw signals and the base

vectors of the corresponding detail subspaces.

Figure 4: EMG- decomposition and pattern clustering with

adaptive wavelet / k-mean algorithm.

6 ADAPTIVE WAVELET

DECOMPOSITION

The EMG is decomposed in a number of levels (dif-

ferent resolutions) of an appropriate wavelet basis.

OPTIMIZATION OF EMG-SIGNAL SOURCE CLASSIFICATION BASED ON ADAPTIVE WAVELETS K-MEAN

ALGORITHM

493

The Daubechies wavelet db5 from Daubechies

(Thompson et. al, 1996; McKeown et. al, 2002) has

been used with 5 decomposition levels. The wavelet

coefficients are roughly classified into two different

classes: a burst zone where artefacts and myoelectric

signals coexist and an inter-burst zone where only

artefact contribution is present. By using hard

thresholding the high-frequency components are set

to zero. In cases where there is no artefact superim-

posed to the myoelectric signal and associated

MUAP’s potentials, the coefficients are supposedly

lower so they will be set to zero with higher prob-

ability. The noisy components of the wavelet de-

composition are truncated and the signal is recon-

structed from the remaining components, addition-

ally the MUAP’s mapping feature with adaptive

wavelets reflects an accurate definition of pre and

post-firing interval identification with related

movement of the subjects (McKeown et. al, 2002;

Fang et. al, 1999).

7 ROC PERFORMANCE

ANALYSIS

As EMG signal inherited a vast number of noise

interference, this will affect result of clustering and

then need to characterize EMG sensor itself for

calibration and buffering purpose. Receiver operat-

ing characteristics (ROC) curve, is analysis of sensor

signals clustering were it is calculated through re-

peatable EMG recording which tend to be classified

in a specific classification algorithm (Jung et. al,

2001). The robustness of wavelet/k-mean algorithm

was tested in contrast to the amplifier gain of inter-

face (Andrzej Cichocki and Shun-ichi Amari, 2003)

Intramuscular EMG electrode which is used in clini-

cal experiment is reusable and composed of lined

conductive area used for increasing measurement

stability and reduction of parasitic noise associated

with physiological measurement session. The defini-

tion of sensitivity and selectivity with ROC analysis,

have the following criteria for recursive data cluster-

ing and pattern classification of biomedical and

clinical data.

As table 1. illustrated that the average efficiency

of classified MUAP’s potential in related EMG

signal , the obvious maximum asymptotic properties

Ω

EMG

(t) of 0.97716 and of minimum one of

0.011208 and this reflects high contrast between the

recorded EMG potential ,in which can be considered

as differentiated parameters in classifying associated

MUAP’s signal. For further investigation of this

effect, additional analysis was applied to the classi-

fied EMG signals using the non-negative matrix

decomposition after a k-mean clustering stage, in

which a relevant result of the EMG classification

shows the approximated results in relation to the

MUAP’s intensity. A performance test was applied

to the 9 clustered patterns, by which illustrate that,

the same maximum and minimum asymptotic prob-

abilities for the verified EMG patterns, which in

corresponding 25-test pattern that presented only a 3

EMG-MUAP’s pattern with relevant high voltage

intensity (A. J. Bell and T. J. Sejnowski, 1995),

(Kadefors et. al, 1999).

As observed from the performance index of

adaptive wavelet decomposition could be noticed a

well discriminated EMG pattern such as low firing

contraction, mid –firing contraction , and high firing

contraction and other elated MUAP’s biopotential

action signals associated with muscular fibers firing

schemes (Andrzej Cichocki and Shun-ichi Amari,

2003), (Micera et. al, 2001).

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Sensitivity of EMG electrode

Specificity of EMG pattern

ROC curve

Figure 5: ROC curves analysis of 24 EMG pattern using k-

mean clustering algorithm after wavelet decomposition of

MUAP intensity patterns.

Table 1: ROC curve analysis of EMG signal patterns

based on wavelet k-mean clustering technique*.

EMG

pattern*

Area

Under

curve

Std.

Error

Asymptotic

Prob

Ω(EMG)

95.%

LCL

95.%

UCL

EMG1

0.23013 0.1632 0.1083 -0.08975 0.55

EMG2

0.09751 0.29079 0.16498 -0.47243 0.66745

EMG3

0.3714 0.08492 0.08572 0.20496 0.53785

EMG4

0.5083 0.49786 0.97716 -0.46749 1.48409

EMG5

0.60189 0.19039 0.48479 0.22873 0.97505

EMG6

0.49024 0.23233 0.95367 0.03489 0.94559

EMG7

0.12033 0.32535 0.19027 -0.51734 0.758

EMG8

0.53122 0.19993 0.81122 0.13937 0.92307

EMG9

0.33194 0.20502 0.31729 -0.0699 0.73377

EMG10

0.59129 0.4909 0.75282 -0.37085 1.55342

EMG11

0.51452 0.49979 0.96004 -0.46505 1.49409

EMG12

0.09066 0.1331 0.01486 -0.17021 0.35152

EMG13

0.2125 0.24776 0.16159 -0.2731 0.6981

EMG14

0.57797 0.09307 0.42753 0.39556 0.76038

EMG15

0.73418 0.10483 0.07323 0.52872 0.93964

EMG16

0.28801 0.15334 0.20715 -0.01253 0.58854

EMG17

0.48729 0.05748 0.91535 0.37463 0.59995

EMG18

0.03942 0.19281 0.11208 -0.33849 0.41732

*18 subject were tested in the vicinity of ROC curve analysis.

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0 5 10 15 20 25

SIRs [dB]

Index of column in the original mixing matrix - A

Run : mean(SIR) = 2.0457 [dB]

Figure 6: Entropy index of EMG-wavelet k-mean decom-

position algorithm for 24 EMG pattern.

8 EVALUATION OF EMG

SIGNAL DECOMPOSITION

PERFORMANCE

Verification of the accuracy of an intramuscular

EMG signal decomposition requires the availability

of signals for which the decomposition result is

known and the definition of quantitative indexes that

allow comparison of performance. Moreover, for

completeness and to assess robustness, the perfor-

mance of a specific algorithm should be evaluated

based on a number of signals of different complexi-

ty. The reference results were obtained by manual

decomposition of a number of experimental signals

by expert operators. However, different patterns may

result when the same or different operators attempt

to decompose the same signal twice, especially if the

MU firing rates are irregular, the MUAPs are simi-

lar, superposition of MUAPs are frequent, and some

MUs may be intermittently recruited (Farina et. al,

2001; Kadefors et. al, 1999).

In addition different wavelet algorithms may

weigh different information, such as waveform simi-

larity or firing regularity; differently and therefore

produce different results. Furthermore specific algo-

rithms may be more appropriate in certain cases and

others in other cases. To assess accuracy, DeLuca

(Andrzej Cichocki and Shun-ichi Amari, 2003) pro-

posed to detect signals (using multiple electrode

surfaces) from the same MU at different locations

and to compare the results of the decomposition of

the two signals obtained. This way the probability of

incorrectly decomposing the different signals and

yet having the same firing pattern for an investigated

MU is low. When the decomposition results agree

for all the channels, the decomposition is considered

correct. The variability degree of different wavelet

algorithm was calibrated with each EMG–sensor

channel with reference ROC curve as illustrated in

Fig.5. The reference decomposition result can also

be obtained from synthetic signals generated by a

model. In this case the crucial issue is to describe all

the relevant characteristics of the experimental sig-

nals. A model is the only way to test the algorithms

with signals having selected characteristics in order

to evaluate the sensitivity of the decomposition algo-

rithms to different EMG signal parameters. Whatev-

er the approach for the generation of reference de-

composition results, it is necessary to introduce

indexes of performance computed from the compari-

son of the results obtained by the application of the

algorithm under test and the reference. Fig.8 that and

Fig.9 which illustrated the k-mean separation hyper-

plan for 24 MUAP’s signal recorded as synchro-

nized EMG recording system.

Figure 7: 3D Performance index of decomposed EMG

signal based on wavelet algorithm indicate main compo-

nents of 9 EMG extracted pattern from the 24 overall

EMG patterns.

Figure 8: k-mean clustering result for 24 MUAP’s record-

ed in needle EMG electrode, illustration of white cross and

red block pattern in the EMG signal for corresponding

MUAP’s clusters.

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9 RESULTS AND DISCUSSION

Adaptive Wavelet-decomposition for EMG signal

illustrates optimality in clustering efficiency of

about (p=0.0128) for spontaneous EMG vector clas-

sification. Some deviation was reported with the

linearity of MUAP classes due to different standard

deviation (SD), of each recorded EMG signal. The

signal deviation can be compensated by increasing

the correlation index, or selecting the same order

number of (Finite impulse response) FIR filtering

module to attenuate the parasitic noise in the EMG

transmission pathway.

Testing additional wavelet/ k-mean algorithms to

evaluate clustering efficiency presents with robust

hyperplane classification based on other criteria such

as EMG signal turns, spike area, integrated area, and

phases. The tested classes that have been presented

in fig.9 also shows maximum intensity differntiality

performed in adaptive wavelet algorithm as concise

effective methods to increase stability of overall

clustering schemes. Euclidean distance have been

computed in background algorithm as state vector

mapping (SVM) matrices for each EMG signal with-

in individual channel in contrary this will overload

computation time for reiterative clustering.

Figure 9: Selective MUAP’s classed that corresponding to

maximum spike activity pattern in recorded EMG signals.

As fig.10 illustrated the clustered coefficients of the

MUAP’s potential signal cab be differentiated in

accordance to the maximum intensity which in this

case considered as discriminative characteristics for

clinical classification system. Odd clusters as it

shown in fig.6 that have been selected for compara-

tive purposes to localize the pre and post-firing myo-

fibers relative to subject movement. Prospective

clustering data for MUAP’s based on k-mean clus-

tering technique that can be demonstrated in fig.7

where

Figure 10: Clustered coefficients of MUAP’s signal using

wavelet decomposition and K-mean clustering using 18

EMG samples.

10 CONCLUSIONS

The decomposition of the intramuscular EMG signal

is a complex task that involves advanced signal

processing and pattern recognition techniques. Their

application covers the fields of basic physiology,

neurology, motor control, ergonomics, and many

others. Current available techniques and the pre-

sented wavelet- k mean allow reliable decomposi-

tion at low /medium force contraction levels during

short and long contractions in static and dynamic

conditions. The availability of such methods for

automatic intramuscular EMG signal analysis allows

the completion of experimental studies that were

unthinkable some years ago, such as the investiga-

tion of MU activity during very long contractions

(up to hours). Intramuscular EMG signal decomposi-

tion is, however, still carried out mainly in research

environments while it finds limited clinical applica-

tion. This is mainly due to the limitations that EMG

signal decomposition still has, such as the amount of

time required to obtain clinically useful information

(especially if high reliability on a number of condi-

tions is required), the necessity in most cases of an

interaction with an expert operator, the applicability

to only low-to-medium contraction levels, and the

need of specially trained persons for the proper posi-

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tioning of the needle electrode to obtain the high-

quality signals required for reliable decomposition.

These limitations are being addressed by current

research efforts. The obtained result of this work and

other related work could be contribute to optimize

the efficiency and reliability of intramuscular EMG

signal

ACKNOWLEDGEMENTS

We acknowledge Aachen University of Applied

Sciences, RWTH-Aachen University and DAAD

(Deutsche Akademische Ausländische Dienst) for

providing accessibility with financial and scientific

support to put this work on the track of success.

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