ELSEVIER
Journal
of Neuroscience
Methods
56 (1995)
43-47
Application of polynomial regression modeling to automatic
measurement of periods of EMG activity
Kenji Takada a**, Kohtaro
Yashiro
‘, Toshifumi
Morimoto
’
” Departments
of Orthodontics,
Faculty of Dent&y,
Osaka lJnit,ersi&.
1-X Yumaduoku,
Sttitu. Owka 56.i. .la,w~
h Oral Physiology,
Faculty of Dentistyv
Osaka Unir-ersity,
I-H Yamadaoku.
.Tuita, Osnka 566, .!ap,tm
Received
29 May
1993; revised
31 January
1994: accepted
26 MAY 19Y4
I
.--.
-_-
.__----
~. ._.- _..__. -. ____-.-
Abstract
We have developed a new algorithm for automatic detection and measurementof on/off periods of EMG burst and
examinedvalidity and reliability of the measuringtechnique. Mean EMG amplitude (a) during a semi-stationarystate of an
EMG data array {EMG) is calculated. BecauseI@ was determined to be significantly correlated with g(7;,,) ior R(T,,*)) which
represent amplitude on a polynomial regressioncurve g(t) which best-fitted to the (EMG}, the estimateg(TL”) (or &Tend)) is
calculated by substituting a into a regressiveequation f(a) which explains the associationbetween the ;M and g(T,,) (or
dT,,J). To, and Tendare human-determinedon/off burst times for the (EMG}. The on/off periods of the EMG burst are
finally computedasroots of the gft) when g(T’,,) and g(Tend)are subtractedfrom the constantof the polynomial. Application of
the current method to the human masticatory muscleactivity during chewing revealed that the absolutedifferences between
human-and computer-determinedmeasurementswere smallerthan 10 ms, and thesemeasurementsdid got differ significantly.
We concludethat the proposedalgorithm is useful and effective for automatic detection and measurementof on/off periodsof
EMG burst.
Ke.vw0rd.c Automatic measurement;Electromyography; Mastication; Polynomial regression
.I__.--.~_
1. Introduction
Computer-based automatic analysis of EMG records
(Geister and Ash, 1975; Hannam et al., 1977; Takada
et al., 1988; Turker et al., 1989; Marple-Horvat and
Gilburg., 1992; Takada et al., 1992) enables minimization of variation between measurements by different
individuals by incorporating common decision criteria
(empirical knowledge) among multiple operators. The
reproduction of measurements is further improved,
since measurement error of a single operator becomes
nil. In addition, digital signal processing rejects errors
associated with scaling of time and amplitude which
are inherent in visual inspection of an analogue signal.
Automated EMG measurement also simplifies laboratory work. Previous algorithms Wkon, 1982; Takada et
al., 1988; Higashi, 1989) for automatic measurement of
on/off periods of EMG burst for masticatory muscles
* Corresponding
owe.
author.
Elsevier Science B.V.
ssDlo165-1~370~94~0008x-x
Tel.:
6-876-57-11,
ext. 2223;
Fax:
6-878-
.-..-.-
... .-.-.----.
~-
during chewing are, in general, such that a time bin of
minimum EMG activity is selected empirically in a
masticatoty cycle to calculate 3 slice level, e.g., mean
response plus its standard deviations and the time
point at which signal amplitude surpasses the slice
level in terms of probabilities is assumed to be the time
of onset (or end) of EMG burst. This idea may be
feasible for muscles which show distinct separation
between bursting and resting periods of- activity. However, such algorithms may not be effective when they
are applied to the jaw-opening muscles or lip muscles
which are activated in the jaw-opening phase (Nakamura and Kubo, 1978; Sahara et al., 1988). The reason
is that these muscles do not have central inhibitory
phases and thus do not necessarily show abrupt in-
crease or decrease in activity. Instead. they show occasionally irregular and large fluctuations in amplitude
during the jaw-closing and intercuspal phases, thus
increasing the values for the mean and SD to determine the slice level. The results of automatic measurement often do not coincide well with the human visual
and/or intuitive judgment. A further problem inherent
44
K. Takada
et al. /Journal
of Neuroscience
in the previous algorithms is that rationale bases on
which these parameters such as the time bin for minimum activity and the slice level are chosen and statistical assessment regarding measurement reliability have
not been fully provided.
The purpose of this report is to propose a new
algorithm for automatic detection and measurement of
periods of onset and cessation of burst in EMG records
and examine validity and reliability of the measuring
technique. For this purpose, we attempted to apply
polynomial regression modeling.
2. Methods
2.1. Algorithm
Let us define, in EMG records, the time period
when a significant burst of EMG activity is seen as a
non-stationary state, while the remaining period as a
semi-stationary state. The beginning of the burst then
coincides with the end of the semi-stationary
state,
while the end of burst signifies a transition from the
non-stationary
to the semi-stationary
state. In the
semi-stationary state, the EMG amplitude mostly reveals minimum activity but may occasionally show relatively larger and irregular fluctuations. A primary assumption we have made is: in visual determination
of
on/off periods of EMG burst, human operators make
virtual smoothing or a kind of curve fitting to an EMG
trace and then search for unique points of transition
from the semi-stationary state to the non-stationary
state (or the reverse) on the virtual curve. A model for
automatic detection and measurement of onset and
end of EMG burst is then described in the following
algorithm from Step 1 to Step 3 (Fig. 1):
Step 1
A digitized EMG data array {EMG} is produced.
Mean EMG amplitude a during the semi-stationary
state is calculated for the duration D X J by averaging
multiple amplitudes which are selected from the (EMGJ
in ascending order from the minimum
in terms of
amplitude. D is the duration of the {EMG}. J is a
constant given as a mean ratio of the duration of the
semi-stationary state to the corresponding D and determined in advance by human inspection for a sufficient number of (EMG}s of a motion which is the
object of investigation.
Step 2
Provided that a function g(t) is the optimal polynomial expression of an EMG data array {EMG), and the
time T,, (or Tend) is a human-determined
time of onset
(or end) of activity for the {EMG}. If we know a
regressive equation f(a) which accounts for the signif-
Methods
56 (1995)
43-47
42
R,
0
Time A
(Ton)
D
Fig. 1. A diagram which illustrates autohatic measurement of onset
of EMG burst by the current algorithm. (EMG), digitized EMG data
array which shows periodic activity; a, mean EMG amplitude during
a semi-stationary period; g(t), polynomial regression curve; D, duration of the {EMG); g^(T,,), estimated amplitude by substituting @
into the regressive equation f(R); A, computer-determined time of
onset of EMG burst.
icant association between the g(T,,,) (or g(T,,,)) and
a, we obtain a good estimate k(T,,) (or &Wend)) for
the {EMG) by substituting II? into the equation.
Step 3
The time of onset (or termination) of EMG burst is
calculated as the root of the polynomial equation g(t)
when g(T,,) (or SW,,,)) is subtracted from the constant of the polynomial (Fig. 1).
2.2. Experimental implementations
To evaluate the validity of the proposed algorithm,
the associations between I@ and g(T,,) and g(T’,,)
were investigated. Materials comprised of a 20 chewing
cycle data set of surface EMG and jaw movement
trajectory signals sampled from an adult male subject
by a computer (PC386GS, EPSON, Tokyo) during unilateral deliberate gum-chewing. The EMG data were
recorded from the anterior temporalis (AT) muscle
(jaw-closing muscle) and the inferior orbicularis oris
(01) muscle (lower-lip muscle).
EMG signals were recorded by means of Beckmantype paired silver surface electrodes (8 mm diameter)
with an inter-electrode distance of 1 cm. The skin was
cleaned with alcohol swabs and lightly abraded by
rubbing with a skin preparation gel (skinPure, Nihon
Kohden, Tokyo) for reduction of electrode-to-skin
impedance. The electrodes were filled with a saline
contact paste (Elefii, Nihon Kohden, Tokyo) to increase the conductivity and reduce electrode-to-skin
resistance. The EMG signals were recorded through
buffer amplifiers (JBlOlJ, Nihon-Kohden,
Tokyo) and
input amplifiers (AB-651J, Nihon-Kohden,
Tokyo).
Each amplifier had a 3-dB-point frequency response of
0.08 Hz and 10 KHz, and a fixed gain of 1000, respectively. The common mode rejection under operating
condition was better than 80 dB. Artifacts were filtered
at frequencies of 15 Hz and 3 kHz. The output noise
was less than f5 mV peak to peak, equal to an input
signal of +5 FV at 10 kHz. The input amplifiers were
K. Takada
et al. /Journal
of Neuroscience
electromagnetically
shielded so that the overall noise
level was less than i 1.0 mV, corresponding to an
input noise of _+1.0 pV. The EMG signal fro’m masticatory muscles has predominant frequency of approximately 160 Hz (Duxbury et al., 197.5) with the upper
limit of 1 kHz (Basmajian, 1978). Based on these
factors and the Shannon’s sampling theorem (Garderhire, 1964), the sampling frequency in the current
system was determined
to be 2 kHz. The sampled
EMG signals were finally absolute-valued for full-wave
rectification, acd averaged with a moving interval of 1
ms and a window time of 5 ms.
Jaw displacement was recorded simultaneously by
means of a mandibular kinesiograph (Model K5, Myotronics Research, USA). Output signals from the kinesiograph were corrected by means of a non-linear
interpolation method (Nagata et al., 1991).
The EMG data array for each masticatory cycle
were automatically divided into open, close, and intercuspal phases to obtain {EMGls. Here, the {EMG} was
a single or a combination of multiple phase data for
each masticatory cycle. The slice level to determine the
beginnings of the open and intercuspal phases was
defined as the vertical jaw position of 2 mm below the
centric occlusion (CO) position, i.e., the origin. The
beginning of the jaw-closing phase was defined as the
time of the maximum jaw-opened position. The time of
onset was searched in the {EMG} which consisted of
the jaw opening and closing phases for the AT muscle
and of the intercuspal phase for the 01 muscle. The
time of cessation of activity was searched in the intercuspal phase for the AT muscle and the opening and
closing phases for the 01 muscle. These combinations
of phases were determined on the basis of observation
of digitized EMG traces.
A mean EMG amplitude M was calculated from
L> XJ data points for each {EMGJ. The Js had been
determined from EMG traces of 20 chewing cycle data
for each muscle (see the second experiment below).
The {EMG) was then treated to a process of polynomial curve fitting to provide a polynomial regression
equation g(t). The optimal polynomial was selected
heuristically from among the 9 polynomials (from the
1st up to the 9th order) by computing the prediction
sum of squares (Allen, 1974) for each polynomial. Then
EMG responses g(T,,l
and g(7”,,) which corresponded to the operator-determined
on/off periods
CT,, and Tend) of burst were calculated. T,, and Tend
were measured visually on EMG traces. Finally, simple
correlation coefficients and linear regression equations
between the g(T,,) and g(T,,,) and corresponding as
were determined for each muscle.
To evaluate the reliability of the current measuring
technique, the on/off
periods of EMG burst determined by the computer were compared with those
identified visually by a human operator on EMG traces.
Methods
56 (19951 4.347
45
Hard copies of the digitized EMGs of single chewing
cycles were provided with a time scale of 1 mm = 2 ms
and a calibration of 1 mV = 50 cm. The EMG traces
were about 35 cm (horizontal) x 15 cm (vertical). The
copies for visual assessment consisted of two sets of 40
EMGs recorded for the aforementioned
muscles (20
strokes by 2 muscles) and arranged in a random order.
These records were obtained independently from those
used in the first experiment. An experienced oral physiologist who had not been informed of the design of
the current experimental paradigm marked the aforementioned time points with a pencil on the EMG
traces. Eventually. the operator examined the same
EMG pattern twice for each chewing cycle. Mean
absolute differences between the time periods measured by the operator and the computer were calculated for statistical comparison of the two methods
(Walpole and Myers, 1978).
3. Results
The association between the polynomial regression
equations and corresponding original EMG records
were 0.43 (onset) and 0.46 (end) for the 01 and 0.53
(onset) and 0.64 (end) for the AT (all P < 0.0001) in
terms of coefficients of determination.
Table 1 gives correlation coefficients between as
and the g(T,,) and g(T,,,). their probabilities
for
significance and regressive equations. As shown in Fig.
2, the mean EMG responses during the semi-stationary
periods significantly correlated with those calculated
on the best-fitted polynomial
curves at the on/off
times of burst which were visually identified on the
EMG traces.
Fig. 3 typically exemplifies good fitness of the polynomial regression curves to the EMG signal. Periods of
start and end of EMG bursts detected by the computer
and the human operator are also indicated. Mean
absolute differences between on/off periods of bursts
measured by the two methods ranged between 4.9 ms
and 9.6 ms (Table 2). The AT muscle revealed mini-
Table 1
Correlation coefficients between mean EMG response # calculated
for the semi-stationary state and EMG responses g(T,,) and g(T,,,)
determined on polynomial curves. their probabilities for significance
an’d regressive equations
Regressand Correlation
P
Regressive equation
coefficient
goAL)
go~(Ten~ )
0.9008
~AT(T,")
0.8017
0.7692
~AT(T~J
0.7176
o.ooo1
0.0001
0.0001
0.0004
g&l=
g^(Tend)=
1.1720 R+2.3181
1.1313 R+3.2280
1.4565 z@+ 1.9878
g^(T,,)=
g*(Tend) = 2.4192 if+
-II_-..,
Number of chewing cycles for statistical analysis is 20.
5.9544
46
K. Takada et al. /Journal of Neuroscience Methods 56 (1995) 43-47
Table 2
Mean absolute differences and their standard deviations between the
time periods identified by a human operator and a computer
Absolute difference (ms)
Muscle
Period of
EMG activity
Mean
SD
01
AT
Onset
End
Onset
End
9.6
7.5
5.3
4.9
6.8
5.8
5.2
2.9
Number of chewing cycles is 20.
Mean Semi-stptiowy
EMG R~ponse
Mean Semi-stalionary
EMG Rospohse
Fig. 2. Scattergrams which represent associations between mean
semi-stationary EMG responses MS and the EMG responses g(T,,)
and g(Tend) calculated on the best-fitted polynomial curves at the
on/off period of EMG bursts which were identified visually on the
digitized EMG records {EMGIs in the first experiment. Number of
chewing cycles for statistical analysis is 20.
mum differences both for the onset and end periods.
The periods determined by the operator and the computer did not differ significantly.
4. Discussion
Previous techniques (Ukon, 1982; Takada et al.,
1988; Higashi, 1989; Marple-Horvat
and Gilburg, 1992)
IVTERCUSPATION
/
n
g eon)
n
HTend)
Coin
coout
Fig. 3. Computer-drawn diagrams which represent the fitness of the
polynomial curves and the on/off period of the EMG burst measured by the computer (blank arrows on the abscissa) and human
operator (black arrows on the abscissa). A digitized EMG signals
(shadow areas) and corresponding polynomial regression curves (solid
curves) are superimposed. Simultaneous records of the two muscles
during the intercuspal phase in a single chewing cycle are exemplified. a, mean amplitude of semi-stationary EMG bursts; g(T,,),
2(Tend): estimated amplitudes by substituting @ into the regressive
equation f(R).
are useful when they measure on/off periods of EMG
bursts which are preceded by or follow a period of
absence of activity. However, the performance of these
methods may be degraded when they are applied to
the jaw-opening and lip muscles, since the method
depends on there being consistent near-zero values for
the signal during EMG quiet periods. Our method
does not have such constraints. In developing the current algorithm, we started from an assumption that
detection of the on/off periods of EMG burst could be
treated by feature extraction of the EMG pattern. The
EMG pattern of a temporal phase from a semi-stationary or less-active state to a significant burst (or the
reverse> is characterized as a virtual nominal pattern by
means of polynomial regression around which a set of
actual EMG data are distributed statistically. In favor
of our assumption, the EMG amplitudes determined
on the polynomial
regression curves which corresponded to the time points judged by the operator
revealed strong correlations with the mean amplitudes
of the semi-stationary state. Because, on average, the
EMG responses on polynomials were 3.9 PV to 4.3 PV
(approximately 5% of the peak amplitudes) higher than
the MS for any time variables examined, it appears that
human operators extract periods at which amplitudes
are slightly higher than that of the semi-stationary
state. In other words, the periods of onset and cessation of burst may be recognized shortly after (onset) or
before (end) the actual on/off times. In contrast, a
previous method (Nakata et al., 1985) which measured
temporal changes in EMG amplitude by determining
the slope of summation curves tended to detect the
onset time earlier and the end time later than the
visual judgment. The computer-determined
duration of
activity was 18% longer than the human judgment. In
our technique, mean absolute differences between time
periods detected by the human operator and the computer were smaller than 10 ms. Since most of the
chewing cycle revealed durations ranging between 600
ms and 800 ms, the results mean that the difference
between the human- and computer-deterrnied
time
periods were around 1.5% with respect to the chewing
cycle under observation. We think this is fairly accurate
if we take into account resolution accuracy of the
K. Takada
et al.
/Journal of Neuroscience
current EMG traces used for visual judgment. In a
strict sense, our algorithm
is not responsible for
whether humans actually follow polynomial
regression-like decision criteria for feature extraction of EMG
patterns. However, the fact that the measurement of
border time points (onset and end) on EMG patterns
by our algorithm almost coincided with that of the
experienced operator who did not participate in developing the logical sequence of the current algorithm
strongly suggests that the proposed method could be
an alternative to the human judgment and could be
effective for automatic measurement of onset and termination and thus duration of muscle activity.
In our technique, the entire process can be treated
automatically
and does not require manual labour,
once the parameters J and f(M) are obtained. These
parameters must be determined beforehand for each
muscle in each individual type of motion to be measured, e.g., chewing, gait, etc. Currently, it takes about
3 min to complete the calculation of on/off periods for
a l-channel EMG data of a single masticatory cycle,
given that all polynomials (from the 1st to 9th order)
are calculated. It was, however, possible to narrow
down the orders. Degrees from the 4th to 6th were
found to be satisfactory as far as the current masticatory muscle EMGs were concerned. Addition of extra
degrees did not necessarily improve the result. Thus,
one can reduce the time for automatic calculation.
Methodologically,
the current technique has the advantage that it can measure not only true onset and end of
EMG burst but also increases and decreases of sustained activity. In this sense, our technique could be
more generally useful than any other computer-based
techniques which are strictly capable only of onset/end
determination.
Finally, it should also be noted that the
current method can be applied to not only human
masticatory muscles during chewing but also other
paradigms which show periodic muscle activity in both
humans and other animals.
Acknowledgements
The authors wish to thank Ms. M. Inaoka for word
processing the manuscript. This study was partly supported by a research fund to Dr. K. Takada from the
Mother and Child Health Foundation.
Methods
56
1199.~143-17
47
References
Allen, D.M. (1974) The relationship between variable selection and
data augmentation and a method for prediction. Technometrics.
16: 125-126.
Basmajian, J.V. (1978) Muscle Alive-Their Functions Revealed by
Electromyography. 4th edn.. Williams and Wilkins, Baltimore.
MD. pp. 37-38 pp.
Duxbury. A.J., Hughes. D.F., Clark, D.E. and Sharpe, 7.C. (lY75)
The bandwidth of masseter electromyograms from bipolar surface electrodes, Electromyogr. clin. Neurophyziol.. 15: 209-221.
Gardenhire. L.W. (1964) ‘Selecting sampling rates’. ISA J. 11: 59-64.
Geister. D.E.. McCall, W.D., Jr. and Ash, M.M.. Jr. (1975) Computerized data acquisition and analysis for real-lime electromyography in clinical dentistry. Proc. IEEE. 63: 14(33-1414.
Hannam, A.G.. Scott. J.D. and DeCou, R.E. ( 1977) A computerbased system for the simultaneous measuremem of muscle activity and jaw movement during mastication i~i man. Arch. @ai
Biol.. 27: 17-23.
Higashi, K. (1989) A clinical study on the relarionship between
chewing movements and masticatory muscle activities. J. Osaka
Univ. Dent. Sot.. 34: 26-63.
Marple-Horvat, D.E. and Gilbey, S.L. (1992) A method for automatic identification of periods of muscular activity from EMG
recordings, J. Neurosci. Methods., 42: 163- 16.7.
Nakamura, Y. and Kubo, Y. (1978) Masticatory rhythm in intraccllular potential of trigemmal motoneurons induced by stimulation of
orbital cortex and amygdala in cats. Brain Res., 148: 504-509.
Nakata. S., Mushimoto. E. and Mitani. H. (1985) DME (deferential
value of jaw movement and burst envelope) automatic analysis
system of electrical jaw muscle activity- during chewing, J. Jpn
Proth. Sot.. 29: 675-6X2.
Nagata, M., Takada. K. and Sakuda, M. t1991) Nonlinear interpolation of mandibular kinesiographic signals by applying sensitivity
method to a GMDH correction model, IEPE Trans. Biometl.
Eng. 3X: 3’6-329.
Sahara, Y.. Hashimoto, N., Kato, M. and Nakamura. Y. (19Xx)
Synaptic bases of cortically induced rhythmical hypoglossal motoneuronal activity in the cat. Neurosci. Res., T: 439-452.
Takada, K.. Nagata. M. and Sakuda. M. (19X8) A personal
computer-based on-line biosignal processing iri clinical orthodontics. J. Osaka Univ. Dent. Sch., 28: X3-97.
Takada, K., Nagata, M.. Miyawaki, S., Kuriyama. R., Yasuda. Y. and
Sakuda. M. (1992) Automatic detection and measurement 01
EMG silent periods in masticatory muscles during chewing in
man. Electromyogr. clin. Neurophysioi., 32: 49’1-505.
Turker, K.S.. Wilkinson, T.M. and Miles. T.S. (10X9) A comparison
of the masseteric silent period in temporomandibular joint dysfunction and normal human subjects by surface elrctromyograpby
and single motor-unit recordings, Arch. Oral Viol., 34: 943-94X.
Ukon. S. (19X2) Automated analyzing system for ,!multaneous measurement of jaw movement and muscle activity during chewing in
man. J. Kyushu Dental Sot.. 36: 1059--10X5.
Walpole, R.E. and Myers. R.H. (197X) Probahilitk and Statistics ti,r
Engineers and Scientists. 2nd edn.. MacMillar?. Nea F’ork. pp
189-737.