c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 314–322

j o ur na l ho me pag e: www.int l .e lsev ierhea l th .com/ journa ls /cmpb

Simulation of surface EMG for the analysis ofmuscle activity during whole body vibratorystimulation

Antonio Fratinia,∗, Paolo Bifulcoa, Maria Romanoa, Fabrizio Clementeb,Mario Cesarelli a

a Department of Electrical Engineering and Information Technologies, University “Federico II” of Naples, Via Claudio,21, 80125 Naples, Italyb Institute of Biomedical Engineering – CNR, Via Salaria Km 29,300, 00016 Monterotondo S., Italy

a r t i c l e i n f o

Article history:

Received 8 March 2013

Received in revised form

7 October 2013

Accepted 8 October 2013

Keywords:

Whole body vibration

Motor unit synchronization

EMG

Motion artifact

a b s t r a c t

This study aims to reproduce the effect of motor-unit synchronization on surface EMG recor-

dings during vibratory stimulation to highlight vibration evoked muscle activity. The authors

intended to evaluate, through numerical simulations, the changes in surface EMG spectrum

in muscles undergoing whole body vibration stimulation. In some specific bands, in fact,

vibration induced motion artifacts are also typically present.

In addition, authors meant to compare the simulated EMGs with respect to real recor-

dings in order to discriminate the effect of synchronization of motor units discharges with

vibration frequencies from motion artifacts.

Computations were performed using a model derived from previous studies and modi-

fied to consider the effect of vibratory stimulus, the motor unit synchronization and the

endplates-electrodes relative position on the EMG signal.

Results revealed that, in particular conditions, synchronization of MUs’ discharge gen-

erates visible peaks at stimulation frequency and its harmonics. However, only a part of

the total power of surface EMGs might be enclosed within artifacts related bands (±1 Hz

centered at the stimulation frequency and its superior harmonics) even in case of strong

synchronization of motor units discharges with the vibratory stimulus.

occurrence of pseudo-sinusoidal artifacts, sometimes referredto as mechanograms [11,19].

1. Introduction

The analysis of surface electromyographic signal (EMG) andits concise parameters (i.e. root mean square – RMS) is a com-mon method of analysis of muscle activity [1–10]. However, in

some situations such as vibratory stimulation, the presence ofartifacts can severely affect the reliability of data [11–14].

∗ Corresponding author. Tel.: +39 0817683786.E-mail address: a.fratini@unina.it (A. Fratini).

0169-2607/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reshttp://dx.doi.org/10.1016/j.cmpb.2013.10.009

© 2013 Elsevier Ireland Ltd. All rights reserved.

Direct application of mechanical vibration to a muscle bellyor a tendon produces a reflex muscle contraction known asTonic Vibration Reflex (TVR) [15–18]. It also induces a rhythmicmovement of electrodes and skin that is responsible for the

Due to its capacity to generate muscles responses, vibra-tory stimulation has been extended to the entire body (the

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hole body vibration or WBV stimulation) and used as specificreatment in physical therapy, rehabilitation and professionalports [20–25].

However, WBV research outcomes are not consistent; a partf the studies suggests there are some muscular improve-ents, whereas others reveal no substantial difference [26,27].The wide variability of WBV effects can be attributed to dif-

erent treatment variables such as amplitude and frequencyf the stimulus, age and athletic condition of individuals

nvolved in trials, stimulation protocols and subject’s posture.oreover, in a very small percentage of the WBV stud-

es, the presence of peculiar motion artifacts is considered11–13,25,28].

In surface EMG (isokinetic, isotonic, gait analysis, etc.)otion artifacts arise from skin stretch, cables movements

r the relative movement of electrodes [29]. The type andharacteristics of electrodes (i.e. capacitive coupling basedlectrodes) may also influence their occurrence and relevance30–32].

The frequency content of motion artifacts is generally con-ned below 10–20 Hz and a band-pass filter (20–500 Hz) isufficient to reject most of the artifact [33].

WBV or direct vibratory stimulation however, producesrtifacts that extend within the EMG band, with a relativeower content hold in very narrow bands (typically a few Hzide) centered at vibration frequency and its superior har-onics. In such a case, the band-pass filtering procedure is

ot suitable for removing the contributions of artifact.The surface EMG spectrum extends in a wider range

between 20 and 500 Hz); thus, in the absence of vibratory stim-lation, the relative EMG power content in the same artifactands results as a small percentage (less than 6%) of the totalower [11]. Then, by assuming the EMG power content not toe relevant in these bands, the use of sharp notch filters cane considered as an appropriate option to remove the artifactoise [11,12,25].

Some authors, however, suppose that, during a WBVession, the synchronous activation of muscle fibers triggeredy the mechanical stimulus at vibratory frequency generates aonsiderable amount of power which appears in the artifact’selated bands [15,19,34–36].

With respect to this scenario, the authors aim to evaluatehe power content of surface EMG of a muscle undergoing WBVy the use of numerical simulations.

Additionally, the authors intend to compare simulationsith respect to EMG recordings to assess signal modification

nd to discriminate the effect of stimulus synchronizationrom motion artifact.

. Methodology

n this study, a three dimensional approach was used toimic EMG response. Surface electromyographic signals were

eproduced as a sum of action potential trains coming fromifferent motor units along the muscle.

Numerical simulations were performed using a modelerived from previous published studies [36,37]. The modelimultaneously consider the discharge patterns of typicaluscle motor units (MUs), the triggering effect appearing

o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 314–322 315

during local vibratory stimulation [19], the generation ofmotor unit action potentials (MUAP) and the influence of therelative position of muscle end plates as well as electrodefiltering [38–41].

In this study, Rectus Femoris (RF) muscle activity was simu-lated, although the general concepts can be extended to othermuscle groups.

The major model elements are detailed in the followingsections.

2.1. Motor unit action potentials, muscle innervationand arrangement of electrodes

MUAPs were computed using the approach proposed by Dim-itrov and Dimitrova [37]. The model was used to consider arealistic intracellular action potential (IAP), the generation oftwo waves that propagate in an opposite direction from theend-plate, the excitation extinction at the fiber ends (respon-sible for the MUAPs terminal phases occurrence) and theaveraging effects of surface electrodes.

IAP spike was identical for all fibers; it was modeled asreported in [37] with duration of 1.75 ms and amplitude of120 mV. Each MU consisted of 240 fibers of finite lengthsuniformly distributed in a territory whose radius was 3 mm.Dispersion of the end plates was ± 3 mm, dispersion of activa-tion time was +1 ms, while that of the fiber ends was ± 6 mm.The diameter of the muscle fibers was set uniformly and dis-tributed in a range of 50–55 �m and a conduction velocity of4 m/s was used in the computations of the MUAPs [42]. Theratio between intracellular and extracellular conductivity wasequal to 3, while that of longitudinal and radial conductivitywas set to 5.

The simulated EMG signals were obtained by consideringthe innervation zones’ spreading of Rectus Femoris muscle[36]. Motor units sites were uniformly distributed in a threedimensional region of 90 mm × 30 mm × 20 mm (fiber length,muscle thickness and muscle width respectively) [41].

For numerical simulations, the center of this region wasidentified as the origin of the reference system. End plateswhich resulted then scattered in an area of x = ±45 mm,y = ±15 mm, z = ±10 mm.

The potentials were computed, using the above-mentionedmodel [37], at the electrode site (square plate electrode witha side size (x, z) of 2.5 mm). Two electrodes were considered:the first placed at x = +10 mm, y = 15 mm, z = 0 and the secondat x = −10 mm, y = 15 mm, z = 0.

Finally, the single differential potentials were formed as thedifference between the potentials of the individual electrodes.The inter-electrode distance was equal to 20 mm.

The illustration below depicts some examples of MUAPsgenerated at square surface electrode level (Fig. 1).

2.2. Motor unit discharges

Discharge patterns of MUs were simulated as suggested by

Lebedev and Polyakov [36] in order to consider the triggeringeffect appearing during local vibratory stimulation.

For each motor unit, the interpulse intervals (IPI) serieswere first modeled in the absence of stimulus.

316 c o m p u t e r m e t h o d s a n d p r o g r a m s i n

Fig. 1 – Examples of motor unit action potentials simulatedat surface electrodes level in case of single differentialdetection system. Centers of MUs territory were located atx = −9.2 mm, y = 5.2 mm, z = −0.2 mm for MUAP 1,x = −14.7 mm, y = 11.8 mm, z = −8.8 mm for MUAP 2 andx = −37.3 mm, y = 12.4 mm, z = 7.3 mm for MUAP 3. Surfaceelectrodes of side size equal to 2.5 mm have their centers inx = ±10 mm, y = 15 mm, z = 0 mm (see text).

e(t) = mi(t) (8)

A normal distribution g(�ti) with mean �Ti and standarddeviation � was adopted to compute successive dischargeinstants. The �ti, equal to (tki − t(k−1)i), defines the typical timeinterval between two successive discharges of the i-th MU.

g(�ti) = 1

�√

2�e−((�ti−�Ti)

2/2�2) (1)

Each motor unit had its mean discharge interval �Ti. MeanIPIs values were obtained from a random variable uniformlydistributed in the range of 90–200 ms (5–11 pulse/s), in accor-dance with the experimental data of Person and Kudina forthe Rectus Femoris; [19] � was set to 30% of the �Ti [36].

The effect of vibratory stimulus was then modeled overeach vibration cycle. The time interval between the stimulus(i.e. the start of the cycle) and the activation of the i-MU wasdenoted with tcycle. The tcycle was computed using a Gaussiandistribution p(tcycle) with mean equal to a half of the vibrationperiod (Tvb/2) and standard deviation Scycle.

The analytical representation of p distribution results in:

p(tcycle) = Ae−((tcycle−(Tvb/2))2/2S2

cycle)

(2)

A = 1∫ Tvb

0e−((tcycle−(Tvb/2))2/2S2

cycle)dtcycle

(3)

with O < tcycle < Tvb

tcycle is then correlated to the time variable t by:

t = tcycle + (n − 1)Tvb (4)

b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 314–322

where n = 1, 2, 3,. A is the normalization coefficient and t is thetime of the next MU discharge which is derived by the sum ofthe (n − 1)-th vibration period added tcycle.

The Scycle (width parameter) establishes the dispersion ofMU firing instants at around half of the vibration cycle. Inthis study, we explored fixed values of dispersion (1 or 3 ms).However, to replicate the spreading effect found at higher fre-quencies by Person and Kudina, we also considered the caseof the variation of the Scycle with the vibration frequency [19].With this aim, Scycle was set equal to 55/Tvb (where Tvb isexpressed in ms).

The complete characterization of the discharge patternsunder vibratory stimulation was finally obtained by multi-plying g(�ti) with p(tcycle), assuming the distributions to beindependent [36].

The h(tki) was then:

h(tki) = B · g(tki − t(k−1)i) · p[tki − (n − 1)Tvb] (5)

B =(∫ +∞

t(k−1)i

g · pdtki

)−1

k≥2 (6)

where n is the number of the vibratory cycle to which tki isreferred, and B is the normalization coefficient.

The initial discharge instant t1i (for each i-MU) was gen-erated by a uniform distribution in the range 0–5 × �Ti (�Ti

being the IPI of i-MU) [11]; the time tki−1 of the k-th dischargewas used as parameter for the subsequent tki computation.The probability density function (PDF) of the new distributionis polymodal as the firing patterns became correlated withvibratory stimulus (see Fig. 2); on the contrary, if the vibra-tory synchronization is not considered, it will be representedby g(�ti).

A high correlation of MUs discharge with vibratory stim-ulus was considered for this study; 90% of MUs dischargeswere modeled with the h(tki) distribution and the rest with thetypical g(�ti) patterns [10]. However, such a high grade of corre-lation could be difficult to achieve with a whole body vibratorystimulation rather than with local stimulation.

2.3. Motor unit action potential train and simulatedEMG

MUAP trains mi(t) were computed as a mathematical con-volution of i-th MUAP shape with its specific firing instantsdistribution:

mi(t) =∑

k

si(t) · hi(t − tki) (7)

where si(t) is the computed potential of the i-th MU, k is thenumber of pulses and tki is the time instant of k-pulse.

The simulated EMG signal is then obtained as the linearsummation of all of the computed MUAP trains:

NMU∑

i=1

where NMU represents the number of simulated motor units.

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 314–322 317

Fig. 2 – Representation of PDF of the MUs discharge intervals distribution h(tki): the left PDF was obtained considering avibration frequency of 23.3 Hz (time period of about 42,92 ms) and a standard deviation of 1 ms (Scycle), the right PDF wasobtained considering a vibration frequency of 44.5 Hz (time period of about 2247 ms) and a standard deviation of 3 ms. Inboth cases without vibratory synchronization h(tki) has a mean discharge time of 110 ms (≈9 pulse per second) and a 12 msstandard deviation (dotted lines).

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To assess spectral modification, different numbers of activeotor units were considered in the generation of EMG signals.

n particular, the effect of 50, 100 and 250 firing MUs was inves-igated in this study. All of the numerical computations wereerformed using MATLAB® with a time resolution of 20 �s.

.4. EMG recordings and signal processing

MG recordings used in previous studies [11] were reportedere in order to compare numerical simulation results withecorded EMG data. In this study, were considered biopotentialecordings collected on Rectus Femoris (RF) of fifteen healthy

ales (age 24.6 ± 4.9 years, height 175.17 ± 5.83 cm, weight4.17 ± 9.09 kg), using small disk Ag/AgCl electrodes (5 mm iniameter, inter-electrode distance of 20 mm) during vibratorytimulation at different frequencies.

EMG signals were amplified using a multi-channel, isolatediomedical signal amplifier (Biomedica Mangoni, Pisa, Italy –odel. BM623; input impedance > 10 MX; CMRR > 100 dB). The

ain was set to 1000 V/V and a band pass filter (3 dB frequency0–500 Hz) was applied. No notch filters were used to furtheruppress line interference. A reference electrode was placedn the ankle of the same leg. A vibrating platform (TSEM S.p.A.,adova – Italy) was used to deliver vibration to the subjects.ibrations provided by the platform were exclusively verticalith an intensity (peak-to-peak displacement) set to 1.2 mm

nd a frequency ranging from 10 to 80 Hz.A set of consecutive 20-s vibrations at different frequen-

ies, spaced with 60 s rest intervals, was delivered to subjectsn a random order: for this study we considered vibratory stim-lation at frequency equal to: 17.9, 23.3, 28.5, 33.9, 39.2, and

4.5 Hz. EMGs were sampled at 1536 Hz.

Spectra of simulated and recorded EMGs were computedsing 10 sec of signal using a standard PSD Matlab Algo-ithm. The algorithm estimates the power spectral density of

a discrete-time signal using Welch’s averaged, modified peri-odogram method. The EMGs were divided into overlappingsections, each of which was de-trended and windowed with aHamming window. The length of each signal section was setto 5 s and the overlap was set to 50%. Relative power content inthree narrow bands (2 Hz wide) centered at f0, 2f0, 3f0, wheref0 is the applied vibration frequency, was explored. Addition-ally, autocorrelation of EMGs, normalized with respect to theirmaximum value, was evaluated to highlight periodic compo-nents in the signals.

3. Results

In numerical simulations, as expected, the presence of vibra-tory synchronization causes motor units to discharge withtemporal intervals regulated by vibration frequencies. Firinginstants are concentrated around multiples of the vibrationperiod in accordance with the results of Person and Kozhina[19].

Fig. 2 reports two examples of the h(tk) PDF trends in pres-ence or absence of vibratory stimuli: vibration frequenciesused for these numerical simulations were 23.3 Hz and 44.5 Hz(equal to the frequencies used in [11]), the average frequencyof MU discharge was set to ≈9 pulse per second (110 ms IPI)with � = 12 ms and Scycle set to 1 ms and 3 ms respectively.

As can be seen in Fig. 2, in the absence of vibratory syn-chronization, the distribution of firing intervals (IPI) has amonomodal profile (see Eq. (5)). If vibratory synchronizationis assumed, distribution becomes polymodal.

The EMGs recorded under vibratory stimulation differ fromsimulated EMGs; real recordings in fact, exhibit a periodic

oscillation, which was less clearly identifiable in the simulatedEMGs (see Fig. 3).

The frequency analysis of the recorded EMGs highlightedthe presence of sharp peaks at vibration frequency and its

318 c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 314–322

Fig. 3 – Example of muscle activity under vibratory stimulus at a frequency of 44.5 Hz: (left) simulated EMG signal obtainedconsidering 250 active MUs and (right) surface recorded RF EMG.

harmonics. In simulated EMG, the peaks were not alwaysdetected and the presence and the amplitude of peaks gen-erally increased with decreasing Scycle values.

As an example, the power spectra from a simulated and arecorded EMG signal are reported in Fig. 4. In this example,a simulated EMG signal, obtained considering 250 active MUsdischarging with Scycle = 1 ms, was analyzed (left spectrum). Inthe figure, sharp peaks are noticeable, in both spectra, at vibra-tion frequency (44.5 Hz) at its superior harmonics. Peaks werealways present and more pronounced, however, for recordedEMGs.

The inspection of autocorrelation computed on simulatedand recorded signals highlighted another difference. In Fig. 5,the autocorrelation of the simulated EMG and that of the EMGrecording are reported. As can be seen, the autocorrelation of

the recorded EMG clearly shows a dominant periodical com-ponent at stimulation frequency, while the same trend is lessmarked for the simulated EMG.

Fig. 4 – Example of spectra of 3 s of muscle activity under vibratoshows the spectrum of simulated EMG signal obtained consideriside shows the spectrum of surface recorded RF EMG (see text).

It is well known that an autocorrelation that persists peri-odically over all delays with the same period is typical of asinusoidal signal immersed in the noise.

Both the autocorrelations that are shown in Fig. 5 presentthis trend.

However, in the recorded signal autocorrelation the sinu-soidal components are predominant at all delays, whereasthe autocorrelation of the simulated signal shows a less clearperiodicity. These results are in agreement with the obtainedpower spectra.

Multiple numerical simulations were performed (30 at eachvibratory frequency) in order to reveal the relative power con-tent of simulated RF activity in the bands in which artifactcontributes are prevalent.

For simulated EMGs, the relative power contribution

found in the three narrow bands (2 Hz wide) centered atthe vibration frequency and its first two superior harmonicsresulted less than 16% (less than 13% on average on all

ry stimulus at a frequency equal to 44.5 Hz: the left sideng 250 active MUs discharging with Scycle = 1 ms; the right

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 314–322 319

Fig. 5 – Autocorrelation computed on 3 s of EMGs with vibratory stimulus at 44.5 Hz (the reader refers to Figs. 3 and 4):s

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tv

srFv

caus1

trt

Fswse

imulated signal (left) and surface recorded RF EMG (right).

ibration frequencies). The higher values were found forimulation considering 250 active MUs with Scycle equal to

ms (15.81% ± 5.65) (Fig. 6). By changing these conditions,umerical simulations provided lower results.

In recorded EMGs however the highest relative power con-ent was around 40% of the total power with an average on allibration frequencies of about 30%.

Scycle resulted a key parameter for the computation. Ittrongly influenced the expression of peaks, and in turn theelative power content, in the spectra of the simulated signals.ig. 7 shows the results in case of 100 active MUs for differentalues of Scycle.

As expected, increasing the value of Scycle parameterauses a drastic decrease of the presence of sharp peakst vibratory frequencies and at their harmonics. In partic-lar, for Scycle equal to 3 ms, the power percentage of theimulated signal in the band of interest was lower than2%.

Several computations were also carried out to determinehe power content of simulated EMGs in the mentioned nar-ow bands without vibratory stimulation in order to highlighthe increase in the case of vibratory synchronization.

ig. 6 – Power content percentage in artifact related bands (2 Hz wuperior harmonics) evaluated for simulated EMGs with differenith respect to real EMGs. Lighter bars represent the results obta

imulated EMGs. All numerical simulations were computed consqual to 1 ms.

The estimated power in those bands was found to be onaverage not higher than 7% of the total power; Fig. 8 summa-rizes the results of this last study.

4. Discussion

Numerical simulations confirmed the influence of the widthparameter (Scycle) in raising the power found in the narrowbands under observation. Lower values of Scycle led to thehigher values of power percentage. Conversely, the number ofsynchronized MUs did not significantly influence the powerfound in the mentioned narrow bands.

Results show that, even in case of a high percentage (i.e.90%) of vibration synchronized motor units (MUs) discharges,the simulated EMG power content in the three narrow bands(2 Hz wide) centered at the vibration frequency and its first twosuperior harmonics was lower than 16% of the total power. In

recorded non-filtered EMGs a much higher power contributionwas found [11].

Moreover, the relative power content due to vibrationsynchronization was found experimentally to decrease with

ide, centered at the vibration frequency and its first twot number of active MUs: 50 (right), 100 (center) and 250 (left)ined for recorded EMGs while darker reports the results foridering 90% of vibration synchronized MUs and a Scycle

320 c o m p u t e r m e t h o d s a n d p r o g r a m s i n

Fig. 7 – Power content in artifact related bands (2 Hz wide,centered at the vibration frequency and its first twosuperior harmonics) evaluated for different values of Scycle:100 simulated active MUs with Scycle equal to 1 ms (blackbars), to 3 ms (white bars), to 55/Tvb – where Tvb is

r

expressed in ms – (gray bars) respectively.

stimulus frequencies above 30 Hz. On the contrary, motionartifact, depending on the local force applied to muscle [29], isproportional to the acceleration and in turn, it raises as vibra-tion frequency raises as also revealed in previous publishedresearch [11].

However, some aspects have to be discussed: in our study,to replicate the activity of Rectus Femoris muscle, we gener-ated MUAPs from a randomly distributed MUs with a meandischarge time in the range of 90–200 ms (5–11 pulse/s); differ-

ent MUs arrangements and discharge intervals may producedifferent results. Furthermore, it is worth mentioning that theglobal effect of vibration stimulation on surface EMG signal

Fig. 8 – Relative power content of the simulated EMGs in therange of ±1 Hz around the vibration frequency and its firsttwo superior harmonics without vibratory synchronization.

b i o m e d i c i n e 1 1 3 ( 2 0 1 4 ) 314–322

is dependent on measuring procedure; the electrode typeand inter electrode distance as well as the relative positionbetween electrodes and muscle fibers or end-plates alsoinfluence the recorded EMGs [43].

Lastly, the type of vibratory device used (i.e. tilting or oscil-lating plates) influences the type of the stimulus applied; itcould also interfere with the overall effects of artifact contrib-utions.

Further studies should be therefore performed to discoverthe impact of such conditions on the recorded and simulatedsignals.

5. Conclusions

In particular conditions, synchronization of MUs firing as aresult of a vibratory stimulus, might generate sharp charac-teristic peaks in the EMG power spectrum at the vibrationfrequency and its superior harmonics. However, only a limitedpart of the total power of simulated muscle activity was foundwithin the artifacts’ related bands, even with strong synchro-nization of MU discharge pattern with vibratory stimulus.

Although the intensity of artifact is difficult to predict, insome cases there is a significant contribution of motion arti-fact on recorded EMG signals [11–13,25].

Therefore, as also suggested by our results, an artifact fil-tering option has to be considered.

Conflicts of interest

None declared.

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