Spectral properties of myoelectric signals from different motor units in the leg extensor muscles
1 Human Performance Laboratory, Faculty of Kinesiology, University of
Calgary, Calgary, Alberta, T2N 1N4, Canada
2 Royal Veterinary College, North Mymms, Herts, AL9 7TA, UK
* Author for correspondence (e-mail: jwakeling{at}rvc.ac.uk)
Accepted 21 April 2004
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Summary |
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Key words: muscle, motor unit, wavelet, size-principle, principle component, PCA, ramped contraction, human
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Introduction |
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Recent developments in myoelectric signal decomposition into
timefrequency space (e.g. Karlsson
et al., 2000; von Tscharner,
2000
) allow a level of detail to be resolved that has not
previously been possible. During both cycling and running movements, bursts of
muscle activity occur at distinct myoelectric frequencies within each gait
cycle (von Tscharner, 2000
;
Wakeling et al., 2001
;
von Tscharner et al., 2003
)
and it has been suggested that these may represent the signals from different
motor units (Wakeling et al.,
2001
). When faster motor units are active within a myoelectric
signal then increases occur in both the mean or median myoelectric frequency
(Wretling et al., 1987
;
Gerdle et al., 1988b
;
Kupa et al., 1995
) and also
the conduction velocity of the MUAPs
(Sadoyama et al., 1988
;
Kupa et al., 1995
). These
results suggest that there is some intrinsic property of the muscle fibre
which can be characterised within the myoelectric signal. We have recently
shown in the rainbow trout, cat and rat that faster and slower motor units do,
indeed, generate distinct high and low myoelectric frequency bands,
respectively (Wakeling et al.,
2002
; Wakeling and Syme,
2002
). However, these initial experiments considered polarised
situations within a muscle by comparing discrete populations of motor unit
types. Mammalian skeletal muscle is commonly mixed with a range of different
fibre types and hybrid fibre types
(Schiaffino and Reggiani,
1994
; Bottinelli and Reggiani,
2000
) and it is likely that a range of different myoelectric
frequencies are generated by the range of different fibre types.
There are different ways for testing the spectral properties of myoelectric
signals from the different motor units in man. Motor units are typically
recruited in a graded manner (Henneman et
al., 1965) from the slowest to the fastest during ramped isometric
contractions (Garnett et al.,
1978
; Andreassen and
Arendt-Nielsen, 1987
). Therefore, if the observation that higher
and lower myoelectric frequencies are generated by faster and slower muscle
fibre types holds true for man it should be expected that the EMG signals
during a graded isometric contraction will contain sequentially higher
frequency components as the faster motor units become recruited. Secondly,
electrical stimulation can generate reversals in the recruitment order with
the faster motor units being preferentially activated
(Kanda et al., 1977
;
Stephens et al., 1978
), and
such contractions can be compared with low magnitude stretch reflexes where
the slowest motor units will be active
(Henneman et al., 1965
). The
purpose of this study was to use these two approaches to elicit varying motor
unit recruitment and to quantify the spectral properties of the myoelectric
signals from these muscle contractions when it could be assumed that different
motor recruitment occurred. It was expected that the changes in myoelectric
spectra would be subtle, and so principle component analysis was chosen as one
method for quantifying the signals, because it is a powerful technique that
can identify systematic changes in spectral properties
(Ramsay and Silverman, 1997
).
The principle component approach was compared to a second technique where the
frequency bands were identified that could distinguish activity from the
different motor units during ramped contractions.
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Materials and methods |
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Electromyography
Myoelectric activity was measured from the quadriceps muscles during
isometric knee extension contractions, and on a separate day from the triceps
surae during isometric plantarflexion contrations. For the quadriceps
contractions the subjects sat on a chair with their thigh horizontal, their
right knee flexed at 75° from full extension, and their ankle strapped to
a dynamometer arm, which had its rotation axis aligned with the
flexion/extension axis of the knee. For the triceps surae contractions the
subjects sat with their foot strapped to the dynamometer arm, which had its
rotation axis aligned with the plantar/dorsiflexion axis of the ankle, the
foot was held vertical, the shank horizontal and the knee flexion angle was
23°. An external force transducer (Omega Engineering, Inc., Stamford, CT,
USA) was attached to the dynamometer arm to measure the torque generated
during knee extension exercises. An oscilloscope monitored the force, and was
initially set with a cursor indicating the maximum voluntary contraction for
each subject. The force was continuously recorded at 3600 Hz on the data
collection computer. The subjects performed isometric contractions, and were
asked to gradually increase the force from zero to maximum (using visual
feedback) over a 4 s period. Each subject performed three such contractions
for the knee extensions and five such contractions for the ankle
plantarflexions with a 2 min rest period between each. This protocol resulted
in no significant difference in the peak force between the contractions.
Contractile force was expressed as % maximum voluntary contraction (MVC).
On a separate occasion the myoelectric signals were recorded in the soleus muscles from the male subjects during reflex contractions. The subjects lay in a supine position with the ankle flexed at 90°. Ten stretch reflexes were elicited by striking the Achilles tendon with a plexor. A further ten Hoffman (H-) reflexes were elicited by using electrical stimulation to the skin overlying the tibial nerve at the crease behind the knee via a bipolar electrode. Each stimulus consisted of a single 0.5 ms pulse (S88 stimulator and SIU 8T stimulus isolation unit, Grass-Telefactor, West Warwick, RI, USA), with the voltage set to the minimum required to result in a twitch of the foot.
Myoelectric activity was measured from the muscle bellies of the vastus medialis, rectus femoris, vastus lateralis, lateral gastrocnemius, medial gastrocnemius and soleus using round bipolar surface electrodes (Ag/AgCl) after prior removal of the hair and cleaning of the skin with isopropyl wipes. Each electrode was 10 mm in diameter and had an interelectrode spacing of 22 mm. Electrodes were placed midway between the motor end point (as determined in pilot experiments) and the distal end of the muscle belly. A ground electrode was placed on the fibular head. The EMGs were preamplified at source (bandwidth 10500 Hz, 3 dB; Biovision, Wehrheim, Germany). Myoelectric signals were recorded at 3600 Hz, on a DAQCard-6062E 12-bit data acquisition card (National Instruments Corp., Austin, TX, USA. Both the EMG amplifiers and the recording computer were powered from batteries in order to minimize 60 Hz noise from external power sources.
Signal analysis
The correlation coefficient, r, was estimated from the peak value
of the cross-correlation function of the raw myoelectric signals between the
muscle pairs for each ramped trial. The myoelectric signals were resolved into
their myoelectric intensities in timefrequency space using wavelet
techniques (von Tscharner,
2000). A set of 13 wavelets was used with center frequencies,
fc, ranging from 7 Hz (wavelet 0) to 542 Hz (wavelet 12).
The intensity is a measure of the time-varying power of the signal contained
within a given frequency band. The intensity spectrum is a close approximation
of a power spectrum calculated using traditional Fourier analysis. A wavelet
domain was defined as the time series of intensity resolved for one wavelet
only. The intensity spectrum for each reflex test was normalized to unit
area.
For the ramped contractions the maximum myoelectric intensity that occurred across all frequencies was identified during each contraction. A threshold was set at 1% of this maximum and the timefrequency coordinates were identified for each peak that exceeded this threshold. The force at which each peak intensity occurred was then calculated. The forcefrequency characteristics were plotted for each peak that occurred in the intensity. For each trial the intensity spectra for each sample point were pooled into bins according to the force level: 515, 1525, 2535, 3545, 4555, 5565, 6575, 7585 and 8595% MVC. The mean spectra were then calculated for each force bin, normalized so the spectrum from the 8595% bin had unit area, and compiled into a matrix of data A.
Principle component analysis of the intensity spectra
The intensity spectra were compiled into a pxN data
matrix A. For this analysis there were N=2160 spectra from the
different force bins, trials, subjects and muscles for the ramped contractions
and there were N=100 spectra from the different stimuli and subjects
for the reflex experiments. Each spectrum contained p=13 intensities
corresponding to the number of wavelets. The principle components of the data
were determined (Morrison,
1967) from the covariance matrix B of the data A.
Briefly, the principle component weightings of the data A are given by
the unit eigenvectors
of the covariance matrix B. The importance
of each component is given by the eigenvalue for each
eigenvalueeigenvector pair, with the greatest absolute eigenvalues
corresponding to the most principle components.
In principle component analysis the mean is typically subtracted from the
data in an initial step, and so the eigenvectors describe the set of
orthogonal components that maximize the variance of the data from the mean
(Ramsay and Silverman, 1997).
In this analysis, however, the mean was not subtracted. Therefore, the
eigenvalues describe the set of orthogonal components that maximize the
variability of the entire data. The most principle component describes the
greatest proportion of the data. The relative proportion of the data explained
by each component is given by
'B
, and the principle
component scores for each component for a given trial are given by
'A.
Statistics
Analysis of variance (ANOVA) was used to determine the effect of reflex
type and subject on the PC II scores for the reflex experiments. Analysis of
covariance (ANCOVA) was used to determine the effect of the muscle type,
subject and contraction force (the covariate) for each of the principle
component, PC, scores. ANCOVA was used to determine the effect of the muscle
type, subject and PC I score (the covariate) for the PC II score. The force at
which the first peak of intensity occurred at each frequency band (wavelet
domain) was determined for each trial. ANCOVA was used to determine the effect
of the muscle type, subject and myoelectric frequency (the covariate) for this
contraction force. Third order polynomial least-squares regression analyses
were performed on the mean forces at which the initial peaks occurred for each
muscle as a function of their myoelectric frequency. The effect of subject
gender on the forcefrequency response of the vastus lateralis muscle
was tested with a general linear model ANOVA in which subject gender, EMG
frequency and a genderfrequency interaction term were used as factors
and the contraction force at which the initial peaks occurred was used as the
response variable. Tests were considered significant at the =0.05
level. Mean values are presented as mean ± standard error of sample
mean (S.E.M.).
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Results |
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The myoelectric activity showed a qualitative increase during each graded increase in contraction force (Fig. 2). Timefrequency analysis showed a graded increase in the high frequency components of myoelectric intensity with the increases in force (Fig. 2D). Careful observation of this intensity plot reveals the transient nature of the bursts of muscle activity. The mean correlation coefficients from the cross-correlation functions between the raw myoelectric signals of the muscles tested are given in Table 1, and were less than 0.17 for the quadriceps and less than 0.03 for the triceps surae.
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The first four principle components accounted for over 95% of the signal (Fig. 3). The first principle component was positive for all frequencies, with its peak weighting between 6090 Hz. The second principle component contained negative weightings at wavelet domain 3 (fc=62 Hz) and below and positive weightings at wavelet domain 4 (fc=92 Hz) and above. The mean principle component scores across all force bins for these principle components are shown in Fig. 4. The PC I scores were positive for all muscles, with higher values for the triceps surae than for the quadriceps. There were small mean scores (although with different signs) for PC II across all muscles, and negligible scores for PC III and for PC IV for some muscles.
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When the mean scores for PC I and PC II for the ramped contractions were plotted then a difference could be detected between the proximal and distal muscle groups (Fig. 5). The quadriceps muscles all showed similar ratios between PC I and PC II scores, with increases in the scores for the higher levels of contraction. On the other hand, the muscles from the triceps surae showed much less correlation between PC I and PC II scores with the level of contraction. When tested across all muscles, ANCOVA (Table 2) showed that the force level contributed significantly to the PC I scores with a positive correlation between force and PC I score. The force level did not contribute significantly to the score for principle components II, III and IV. Analysis of covariance showed no significant covariance between the PC I score and the PC II score.
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Fig. 6 shows that as the
contraction progressed, the initial peaks in intensity at each frequency band
first occurred at lower frequencies and then progressively at higher
frequencies (for k3) as the contraction force increased. ANCOVA
showed that there was a significant effect of the EMG frequency on the force
of these initial peaks, with increases in force correlating to increases in
the myoelectric frequency (P<0.001), and there was a significant
difference in the response between the muscles (P<0.001). The
results for the third order regression analysis of the mean responses for the
six muscles tested (Fig. 7,
Table 3)showed that the
increases in the forcefrequency relationship occurred (section between
the minima and maxima) on average between the frequencies of 80 Hz and 500
Hz.
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There was no significant effect of subject gender on the force levels at which the initial peaks in myoelectric intensity occurred for the vastus lateralis muscle (ANOVA).
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Discussion |
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This study has shown that the principle components PC I and PC II may be used to quantify features of the muscle activity for both graded isometric contractions and also reflex responses. The fundamental properties of myoelectric spectra are conserved across isometric and dynamic contractions, and so it is suggested that principle component analysis will be useful tool for a range of myoelectric studies. PC I had a similar shape to the mean myoelectric intensity spectrum (Fig. 1B) and its scores can be used as an index of the magnitude of the intensity of the muscle contraction. PC II had negative and positive weightings, which transitioned at approximately 100 Hz (Figs 1, 3). The relative scores between PC I and PC II describe the main frequency components of each spectrum, with relatively higher PC II scores resulting in higher frequencies (Fig. 1C). The angle of the PC I-PC II scoring vector in the PC I-PC II scoring plane (Fig. 1C) provides a good measure of the frequency components for these myoelectric intensity spectra.
ANCOVA showed significant increases in the PC I scores as the force level
increased during the ramped contractions
(Table 2), showing that as the
muscle activation was progressively increased then increases occurred in the
fundamental spectral intensity. The mean PC I score was greater for the
muscles from the triceps surae than for the muscles from the quadriceps
(Fig. 4). These mean scores
were based on spectra that had been normalized to the intensity at the maximum
force. Therefore, the distribution of PC I scores in
Fig. 4 indicate that the
triceps surae muscles reached maximal activation levels at lower forces within
each ramped contraction than the muscles from the quadriceps, resulting in a
higher mean score when considered over the whole contraction. These
differences in PC I scores between muscles are consistent with the different
motor unit recruitment strategies that occur between muscles. The smaller,
more distal, muscles recruit all their motor units at lower relative forces
than the larger more proximal muscles and so must rely on increases in motor
unit firing rate to generate the higher forces
(Kukulka and Clamann, 1981;
de Luca et al., 1982
).
Action potentials of the faster motor units have higher conduction
velocities (Kupa et al., 1995;
Sadoyama et al., 1988
;
Wakeling and Syme, 2002
) and
would contribute higher frequency components within the myoelectric signal.
The taps of the Achilles tendon elicit stretch reflexes mediated by the muscle
spindles, which recruit predominantly the slower motor units
(Henneman et al., 1965
;
Garnett et al., 1978
;
Andreassen and Arendt-Nielsen
1987
). The electrically stimulated H-reflex is an analog of the
stretch reflex, sharing common pathways of muscle innervation. However,
electrical stimulation results in the faster motor units being preferentially
recruited (Kanda et al., 1977
;
Stephens et al., 1978
), and
this phenomenom can even occur within the time scale of the reflex
(Burke et al., 1984
). The two
forms of reflex response generate a synchronized motor unit action potential,
but it can be expected that these potentials will be a lower frequency, slower
motor unit, MUAP for the stretch reflex and a higher frequency, faster motor
unit, MUAP for the electrically stimulated reflex. The results
(Fig. 1C) supported this
prediction and demonstrated that the differences in myoelectric frequency that
result from altered innervation patterns can be resolved using the principle
component approach.
During graded muscle contractions the motor units are activated in a graded
manner (Henneman et al., 1965)
with the faster motor units being recruited at higher levels of contraction
(Garnett et al., 1978
;
Andreassen and Arendt-Nielsen,
1987
). It should be expected that the recruitment of faster motor
units at the higher forces results in higher frequencies within the
myoelectric signal. However, previous studies investigating the dependence of
mean myoelectric frequencies on contractile force during graded isometric
contractions showed mixed results, with some papers reporting correlations
(Bilodeau et al., 1991
;
Gerdle et al., 1988a
;
Moritani and Muro, 1987
;
Karlsson and Gerdle, 2001
;
Farina et al., 2002) whilst others reported lack of correlations
(Bilodeau, 1991
;
Gerdle et al., 1988b
;
Onishi et al., 2000
;
Farina et al., 2002a
). One
reason for these conflicting results is that when measuring myoelectric
signals with surface electrodes, the volume conductor effects of the tissue
layers surrounding the muscle play a role in shaping the signal. Indeed, it
has been shown that when the volume conductor effects of the tissues are taken
into consideration then an invarying mean frequency can be accounted for even
when the MUAP conduction velocity increases
(Farina et al., 2002a
). In
this study there was no significant effect of the contractile force on the PC
II scores for the pooled data set, although
Fig. 5 indicates that such a
relation may occur for the quadriceps muscles. Increases in myoelectric
frequency will be reflected in an increase in the scores of PC II relative to
PC I, leading to a smaller angle of the PC IPC II scoring vector (as
illustrated in Fig. 1C). There
was no systematic effect of the contractile force on the angle of the PC
IPC II scoring vector for the ramped contractions in this study
(Fig. 5) and this is consistent
with the studies reporting lack of correlation between force and mean
myoelectric frequency. However, it should be noted that differences in
myoelectric frequency could be discriminated in the reflex experiments
(Fig. 1) despite potential
volume conductor effects, and so the reasons for the lack or correlation
between force and myoelectric frequency during ramped contractions probably
include other factors.
PC III and PC IV have significant scores for some, but not all muscles (Fig. 4). These components, along with the less important components, shape variations in the fine details of the intensity spectra between the different muscles. The raw intensity spectra for any given trial and force bin is the vector product of the principle component scores and the principle component weightings.
Transient frequency bursts during myoelectric activity
We have previously reported that different muscle fibre types produce
signals with distinct myoelectric frequency bands and action potential
conduction velocities for three animal species: the rainbow trout, cat and rat
(Wakeling et al., 2002;
Wakeling and Syme, 2002
).
Mammalian skeletal muscle is commonly mixed with a range of different fibre
types and hybrid fibre types (Schiaffino
and Reggiani, 1994
; Bottinelli
and Reggiani, 2000
), and so it was expected that there should be
myoelectric signals with a graded frequency response during the graded muscle
contractions in this experiment. If it is possible to correlate a myoelectric
frequency to the generation of a particular contractile force during an
isometric contraction then that myoelectric frequency will correspond to the
activity of motor units with a particular excitability within their motor unit
pool.
The myoelectric signal showed intensity at progressively higher frequencies during the ramped contractions (Fig. 2D) and the higher frequency components correspond to the activity from the faster motor units. Careful observation of this figure shows that the myoelectric intensity occurs as a sequence of transient bursts, which can occur at any given frequency. For any given frequency band the bursts of activity occur in an irregular fashion (Fig. 2D), indicating that the motor units, once activated, do not fire consistently through the contraction. It is possible that variability in the firing of each motor unit during ramped contractions contributes to the variability in the instantaneous myoelectric frequency and the lack of correlation between myolectric frequency and contractile force. This would be an interesting avenue for further investigation. Nonetheless, it should be noted that the higher frequency components are only present at the higher contractile forces.
When the forces were quantified at which the peaks in myoelectric intensity
first exceeded the threshold (Fig.
6), there were significant correlations between force and
frequency for all muscles (Fig.
7). These correlations occurred as a positive relation between the
contractile force and the appearance of higher frequency spectral components
in the myoelectric frequency between the frequencies of about 80 Hz and 400 Hz
(Table 3). The
forcefrequency relation showed distinct patterns between the muscles of
the quadriceps and triceps surae. The quadriceps showed a gradual increase in
the force-frequency relation to forces in excess of 70% MVC whereas the
triceps surae showed a much shallower relation, which reached 40% MVC by the
400 Hz frequency. These results are again consistent with the observations
that the larger more proximal muscles can recruit progressively faster motor
units up to higher force levels than the smaller more distal muscles
(Kukulka and Clamann, 1981;
de Luca et al., 1982
).
The myoelectric signals from a motor unit get broadened, and result in
lower frequencies, as the muscle fibres get deeper within the muscle belly,
due to volume conductor properties of the tissues
(Roeleveld et al., 1997).
These effects have led Farina and coworkers
(Farina et al., 2002a
) to
demonstrate that the mean myoelectric frequency can remain constant even when
there is a graded increase in conduction velocity of the constituent MUAPs.
However, in this experiment we can still expect that the progressively higher
frequency components in the myoelectric signal respresent the action of
progressively faster motor units. If part way through the contraction a faster
motor unit was recruited then it would generate higher frequency components in
the myoelectric signal if its muscle fibres were located near the surface
electrodes, otherwise it would result in no frequency increase if its muscle
fibres were located deeper within the muscle belly. Thus, part way through the
contraction the higher frequency elements can only appear if progressively
faster motor units are recruited whose fibres lie near the electrodes. The
results show progressively higher frequencies appearing in the signal
(Fig. 7) which, therefore,
correspond to the progressively faster motor units recruited during all
contractions. The volume conductor model predicts that if there is an
inhomogeneous distribution of different fibre types within the muscle then the
fibre distribution influences the spectral properties of the signal. However,
certainly for the vastus lateralis, it has been shown that the slow type I and
fast type II muscle fibres are randomly distributed across a muscle
cross-section for 30-year-old adults
(Lexell, 1993
), and so in this
experiment the fibre distribution should not be expected to present
confounding factors.
Surface myoelectric recordings have a low spatial selectivity compared with
intramuscular recordings, and so myoelectric signals can be recorded in
locations far from their source
(Lindström and Magnusson,
1977). Therefore, the effect of the cross-talk of both propagating
and non-propagating signals should be considered when making interepretations
about the results from surface electromyographic studies. For propagating
signals, the frequency content depends on the distance from the source, with
the tissues separating the sources from the electrodes acting as low-pass
filters with cut-off frequency decreasing with increasing distance from source
(Lindström and Magnusson,
1977
). Therefore the cross-talk component of the signal will
introduce low and not high frequency components into the myoelectric intensity
spectra and will not contribute to the high frequency components which appear
at higher forces. The effect of cross-talk from propagating signals can be
quantified by the correlation coefficients from the cross-correlation
functions of the raw signals between the muscles
(Winter et al., 1993
). In this
study these correlation coefficients were below 0.17 for the quadriceps and
0.03 for the triceps surae (Table
1), but as discussed above this source of cross-talk will not
influence the forcefrequency results
(Fig. 7). On the other hand,
the non-propagating components of cross-talk may introduce higher frequency
components into the myoelectric intensity spectra
(Farina et al., 2002b
).
However, we can ignore the effect of non-propagating cross-talk in this study
for two reasons. Firstly, the high frequency signal introduced by the
cross-talk increases with the signal amplitude. However, in our ramped
experiment we found no correlation between the scores of PC I, a measure of
the myoelectric intensity, and PC II, a measure of the frequency content, and
so there is no evidence to suggest that the higher frequencies concurred with
the greatest myoelectric intensities. Secondly, Farina and coworkers
(Farina et al., 2002b
)
quantified the magnitude of the non-propagating cross-talk across the
quadriceps muscles when one muscle was electrically stimulated and observed
that the greatest peak-to-peak cross-talk was 8% of the signal magnitude in
the adjacent, stimulated muscle (for a single-differential recording with an
inter electrode distance of 20 mm, comparable to the recording system in this
study). An 8% peak-to-peak value for cross-talk is equivalent to 0.7% of the
signal intensity, a function of the square of the signal
(von Tscharner, 2000
). In this
study we chose a threshold value of 1% of the peak intensity to quantify the
forcefrequency peaks, and this threshold would therefore exclude the
signal components that may be present from non-propagating cross-talk.
The results from this study show that as the contractile force increases
then progressively higher frequency components appear within the myoelectric
signal (Fig. 7). The increase
in force is initially achieved by increasing in the number of motor units
recruited in the muscle, with the motor units being recruited in order from
the slowest to the fastest. Therefore, the higher frequency components within
the myoelectric signal correspond to the activity from the faster motor units.
These results support the previous suggestions that the distinct, transient,
bursts of high and low frequency myoelectric intensity that can be observed
during locomotion (peaking at about 50 and 170 Hz;
Wakeling et al., 2001;
von Tscharner et al., 2003
)
result from activity of different types of motor units
(Wakeling et al., 2001
).
Generation of myoelectric spectral properties
It is commonly proposed that the reason for the higher conduction velocity
of the motor unit action potentials in faster muscle fibres is due to their
larger diameters. This idea can be explained using a local circuit model,
which assumes the fibres are surrounded by a large volume of conducting fluid
(Hodgkin, 1954), and has been
confirmed by in vitro measurements of isolated muscle fibres in a
large volume of conducting fluid
(Håkansson, 1956
).
However, when the external conductance was modeled as being confined to the
interstitial spaces, as in the in vivo condition, it was predicted
that there should be no relation between fibre diameter and conduction
velocity (Buchthal et al.,
1955
). To our knowledge, there is no evidence to indicate that the
MUAP conduction velocity is related to muscle fibre diameter in vivo.
Indirect evidence can be added to this debate using the data in this
study.
Previously published histological studies of the vastus lateralis have
shown that the type I fibres have smaller cross-sectional area than the type
II fibres for male subjects, but are bigger than the type II fibres for female
subjects (Gerdle et al.,
1997). Therefore, if the increase in conduction velocity of the
motor unit action potentials were due to increases in fibre diameter, or area,
then it should be expected that the recruitment of progressively faster motor
units during a graded contraction would result in a gradual increase in the
mean frequency for the male and a decrease in mean frequency for the female
subjects (Karlsson and Gerdle,
2001
). However, it has previously been demonstrated that there was
no difference in the direction of the relationship between mean frequency and
muscle force during ramped isometric contractions
(Karlsson and Gerdle, 2001
).
This was confirmed in this study in that there is no significant effect of
subject gender on the relation between the contractile force and the specific
frequency bands at which the newly recruited fibres are activated. A
dissociation between muscle fibre diameter and the conduction velocity of the
action potentials or mean EMG frequency has also been reported for the
quadriceps in populations with different proportions of each fibre-type
(Wretling et al., 1987
;
Sadoyama et al., 1988
;
Linssen et al., 1991
). The
results from this study, therefore, support previous observations that EMG
frequency can be dissociated from the muscle fibre diameter. It has been
suggested that variation in conduction velocities is more closely linked to
the differences in muscle fibre membrane properties across different fibre
types than differences in fibre diameter
(Buchthal et al., 1955
). This
is supported by direct evidence showing no relation between fibre diameter and
EMG frequency in the myotomal muscle of fish, even when there was a 2.7-fold
increase in EMG frequency between the slow and fast fibres
(Wakeling et al., 2002
).
Conclusions
Myoelectric intensity spectra from isometric ramped and reflex contractions
in the leg extensor muscles can be largely described by two principle
components. PC I describes a fundamental spectral shape and the PC I score is
a measure of the overall intensity of the muscle contraction. The relative
scores of PC II describe the frequency content of the spectra. A greater
relative PC II score occurs during electrically stimulated contractions, when
it was assumed that the higher myoelectric frequencies correspond to the
recruitment of faster motor units. During ramped isometric contractions there
is a progressive recruitment of faster motor units. The PC I scores increased
with contractile force but there was no general relation between the force and
the PC II score. This result is consistent with some of the previous studies
that used the mean frequency of the power spectrum as a measure of the
myoelectric frequency. However, as faster motor units are recruited, higher
frequency components appear in the intensity spectra. The bursts of
myoelectric activity at these higher frequencies correlate with the higher
contractile force, or in the more general sense, with the progressively faster
types of motor unit, which can be assumed to be recruited. Thus, during both
the reflex experiments and the ramped isometric contractions the higher
frequency components in the myoelectric spectra corresponded to instances when
it could be assumed that faster motor units were active.
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Acknowledgments |
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References |
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