Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique
Wavelet analysis of surface electromyogram (sEMG) signals has been investigated. Methods to remove noise before processing and further analysis are rather significant for these signals. The sEMG signals were estimated with the following steps, first, the obtained signal was decomposed using wavel...
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Karan, V. 2019-02-17T17:45:13Z 2019-02-17T17:45:13Z 2015 Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique / V. Karan // Нейрофизиология. — 2015. — Т. 47, № 4. — С. 356-363. — Бібліогр.: 18 назв. — англ. 0028-2561 https://nasplib.isofts.kiev.ua/handle/123456789/148211 612.741.1:519.218.82 Wavelet analysis of surface electromyogram (sEMG) signals has been investigated. Methods to remove noise before processing and further analysis are rather significant for these signals. The sEMG signals were estimated with the following steps, first, the obtained signal was decomposed using wavelet transform; then, decomposed coefficients were analyzed by threshold methods, and, finally, reconstruction was performed. Comparison of the Daubechies wavelet family for effective removing noise from the recorded sEMGs was executed preciously. As was found, wavelet transform db4 performs denoising best among the aforesaid wavelet family. Results inferred that Daubechies wavelet families (db4) were more suitable for the analysis of sEMG signals related to different upper limb motions, and a classification accuracy of 88.90% was achieved. Then, a statistical technique (one-way repeated factorial analysis) for the experimental coefficient was done to investigate the class separability among different motions. Досліджували можливість застосування вейвлет-аналізу щодо сигналів поверхневої електроміограми (пЕМГ). Використання видалення шумів із записів пЕМГ перед обробкою таких сигналів для подальшого аналізу є дуже істотним. Сигнали пЕМГ оцінювалися в наступній послідовності: спочатку отриманий сигнал підлягав декомпозиції з використанням вейвлет-перетворення, потім декомпозовані коефіцієнти аналізувались із застосуванням порогових методик, і, нарешті, виконувалася реконструкція. Попередньо порівнювали ефективність видалення шумів у межах вейвлет-сімейства Daubechies. Було встановлено, що вейвлет-перетворення db4 із цього сімейства виконує знешумлення найкращим чином. Отримані результати вказують на те, що вейвлет-сімейства Daubechies є більш придатними для аналізу пЕМГ сигналів, отриманих в умовах реєстрації різних моторних реакцій м’язів верхніх кінцівок; досягалася точність класифікації 88.9 %. Потім статистична методика (однобічний повторний факторіальний аналіз) застосовувалася щодо експериментальних коефіцієнтів для встановлення якості розділення даних при різних рухах. The author is grateful to Dr. Amod Kumar and Dr. Ravinder Agarwal, PhD supervisors for helping in writing this paper. en Інститут фізіології ім. О.О. Богомольця НАН України Нейрофизиология Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique Класифікація електроміографічних сигналів з використанням аналізу ANOVA, базована на вейвлет-перетвореннях Article published earlier |
| institution |
Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| collection |
DSpace DC |
| title |
Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique |
| spellingShingle |
Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique Karan, V. |
| title_short |
Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique |
| title_full |
Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique |
| title_fullStr |
Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique |
| title_full_unstemmed |
Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique |
| title_sort |
wavelet transform-based classification of electromyogram signals using an anova technique |
| author |
Karan, V. |
| author_facet |
Karan, V. |
| publishDate |
2015 |
| language |
English |
| container_title |
Нейрофизиология |
| publisher |
Інститут фізіології ім. О.О. Богомольця НАН України |
| format |
Article |
| title_alt |
Класифікація електроміографічних сигналів з використанням аналізу ANOVA, базована на вейвлет-перетвореннях |
| description |
Wavelet analysis of surface electromyogram (sEMG) signals has been investigated. Methods
to remove noise before processing and further analysis are rather significant for these
signals. The sEMG signals were estimated with the following steps, first, the obtained
signal was decomposed using wavelet transform; then, decomposed coefficients were
analyzed by threshold methods, and, finally, reconstruction was performed. Comparison of
the Daubechies wavelet family for effective removing noise from the recorded sEMGs was
executed preciously. As was found, wavelet transform db4 performs denoising best among
the aforesaid wavelet family. Results inferred that Daubechies wavelet families (db4) were
more suitable for the analysis of sEMG signals related to different upper limb motions, and
a classification accuracy of 88.90% was achieved. Then, a statistical technique (one-way
repeated factorial analysis) for the experimental coefficient was done to investigate the class
separability among different motions.
Досліджували можливість застосування вейвлет-аналізу
щодо сигналів поверхневої електроміограми (пЕМГ). Використання видалення шумів із записів пЕМГ перед обробкою таких сигналів для подальшого аналізу є дуже істотним.
Сигнали пЕМГ оцінювалися в наступній послідовності:
спочатку отриманий сигнал підлягав декомпозиції з використанням вейвлет-перетворення, потім декомпозовані
коефіцієнти аналізувались із застосуванням порогових методик, і, нарешті, виконувалася реконструкція. Попередньо порівнювали ефективність видалення шумів у межах
вейвлет-сімейства Daubechies. Було встановлено, що вейвлет-перетворення db4 із цього сімейства виконує знешумлення найкращим чином. Отримані результати вказують на
те, що вейвлет-сімейства Daubechies є більш придатними
для аналізу пЕМГ сигналів, отриманих в умовах реєстрації
різних моторних реакцій м’язів верхніх кінцівок; досягалася точність класифікації 88.9 %. Потім статистична методика (однобічний повторний факторіальний аналіз) застосовувалася щодо експериментальних коефіцієнтів для
встановлення якості розділення даних при різних рухах.
|
| issn |
0028-2561 |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/148211 |
| citation_txt |
Wavelet Transform-Based Classification of ElectroMyogram Signals Using an ANOVA Technique / V. Karan // Нейрофизиология. — 2015. — Т. 47, № 4. — С. 356-363. — Бібліогр.: 18 назв. — англ. |
| work_keys_str_mv |
AT karanv wavelettransformbasedclassificationofelectromyogramsignalsusingananovatechnique AT karanv klasifíkacíâelektromíografíčnihsignalívzvikoristannâmanalízuanovabazovananaveivletperetvorennâh |
| first_indexed |
2025-11-26T19:16:08Z |
| last_indexed |
2025-11-26T19:16:08Z |
| _version_ |
1850770759938473984 |
| fulltext |
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 4356
UDC 612.741.1:519.218.82
V. KARAN1
WAVELET TRANSFORM-BASED CLASSIFICATION OF ELECTROMYOGRAM
SIGNALS USING AN ANOVA TECHNIQUE
Received March 20, 2014
Wavelet analysis of surface electromyogram (sEMG) signals has been investigated. Methods
to remove noise before processing and further analysis are rather significant for these
signals. The sEMG signals were estimated with the following steps, first, the obtained
signal was decomposed using wavelet transform; then, decomposed coefficients were
analyzed by threshold methods, and, finally, reconstruction was performed. Comparison of
the Daubechies wavelet family for effective removing noise from the recorded sEMGs was
executed preciously. As was found, wavelet transform db4 performs denoising best among
the aforesaid wavelet family. Results inferred that Daubechies wavelet families (db4) were
more suitable for the analysis of sEMG signals related to different upper limb motions, and
a classification accuracy of 88.90% was achieved. Then, a statistical technique (one-way
repeated factorial analysis) for the experimental coefficient was done to investigate the class
separability among different motions.
Keywords: electromyogram, wavelet, analysis of variance, denoising, classifier.
1Department of Electrical and Instrumentation Engineering, Thapar University,
Patiala, India.
Correspondence should be addressed to V. Karan
(e-mail: karan.una@gmail.com).
INTRODUCTION
In order to use surface electromyogram (sEMG) signals
as a diagnostic tool or control signals, their features are
often extracted before proceeding to the classification
stage. Attempts are being made to improve signal
processing and to obtain more information about the
examined muscles; various techniques have been
applied in classification and processing of sEMGs
[1-3]. In order to predict properties of sEMGs
corresponding to voluntary muscle contractions,
various models have been developed [4].
Wavelet transform was been rather extensively
used in the analysis of EEG, but it began to be studied
with respect to EMG only since last decade [5, 6],
particularly in the engineering application such as
the control of prosthetic devices. Our study was
motivated by the fact that identification of a mother
wavelet function is of the paramount significance
since there is no universal mother wavelet applicable
to all types of the signals [7]. We applied wavelet
denoising technique to remove interference noise
from the signals recorded from the biceps brachii and
triceps brachii muscles of the subjects performing
voluntary contractions. The general wavelet-based
denoising procedures were composed of three steps,
decomposition, determination of denoising wavelet’s
detail coefficients, and reconstruction [8].
Different levels of mother wavelets (db2-db14) of
the Daubechies family were extracted to obtain the
useful resolution components from the sEMG signals.
After removing the noise, time frequency domain
analysis was introduced for analyzing the relation
between voluntary contractions and sEMG signals. The
most effective wavelet for sEMG denoising has been
chosen by calculating the root mean square (RMS) and
standard deviation (s.d.) values. The results showed that
the wavelet function db4 works best among the used
wavelets to remove noise from the sEMG signals. Our
study presents the effects of mean frequency (MnF)
and median frequency (MdF) in the EMG analysis,
especially during voluntary contraction for analyzing
the EMG-muscle force relationship.
Further on, in order to analyze the effectiveness of
sEMG signals for the muscles realizing independent
motions, a statistical technique of analysis of the
variance has been implemented, since it helps to
identify the data pattern and to express these data
in a way to highlight better their similarities and
differences.
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 4 357
WAVELET TRANSFORM-BASED CLASSIFICATION OF ELECTROMYOGRAM SIGNALS
METHODS
SEMG Signal Acquisition and Processing. Three
adult volunteers were involved in the tests. Surface
EMG signals were collected using a routine technique
by noninvasive electrodes from the skin surface of
the elbow joint-controlling arm muscles, mm. biceps
brachii and triceps brachii. A differential amplifier
was used. After amplification, signals were filtered
using the corresponding software and hardware
blocks. In both cases, a high-pass cutoff frequency was
10 Hz, while a low-pass cutoff frequency of 500 Hz
was kept [9-11]. Myograms were recorded from the
above-mentioned muscles at low, medium and high
voluntary contractions under isometric conditions.
The block diagram for the system is shown in Fig. 1.
Denoising Using Wavelet Analysis. Wavelet
transform is a capable transform with a flexible
resolution in both time and frequency domains. The
principle of wavelet denoising [12, 13] consists
of decomposing the signal by performing wavelet,
applying suitable thresholds to the detail coefficients,
zeroing all coefficients below their associated
thresholds, and, finally, reconstructing the denoised
signal based on the modified detail coefficients. The
underlying model for the surface EMG signal, f(n), is
the superposition of the signal, s(n), and noise, e(n),
f(n) = s(n) + e(n) (1)
Once the signal is passed through wavelet
decomposition, a threshold needs to be selected for
estimation of the signal of interest, s(n), from f(n) by
discarding the corrupting noise e(n).
Feature Extraction. As the sEMG signal is time-
and force-dependent, and its amplitude varies randomly
above and below zero values, the analysis becomes
important in a way to define characteristic properties of
the signal. A wide variety of features [14, 15] have been
considered individually and in the group, representing
both the sEMG amplitude and spectral content. So,
feature extraction was done for interpretation of the
recorded signal. All extracted features (s.d., variance,
mean absolute value, etc.) have certain specific
advantages, but the most commonly used technique for
characterizing the power of the signal is the root mean
square (RMS) value. This parameter gives the physical
meaning of the signal, namely the real energy. Another
frequency domain parameter, the median frequency
(MdF), is described as the frequency that divides the
power contained in the signal into two equal halves. The
next parameter, mean frequency (MnF), is an average
frequency that is calculated as the sum of products of
the power spectrum and the frequency divided by the
total sum of the power spectrum.
According to a few authors [16, 17], mathematical
analysis has been done to investigate various
parameters of the power spectral density, and the
above-mentioned frequencies (MnF and MdF) were
found to be most reliable. So, these techniques
have been used in investigating the muscle force
relationship.
RESULTS
Computer-Aided Analysis. In our study, the wavelet
denoising-based analysis was performed by dividing
the signal into two different types; low and high sEMG
signals. The time and frequency domain analyses were
done to analyze the relationship between myoelectric
signals vs. different force levels developed by the
human arm muscles. For this, an analytic study was
initiated to investigate whether the relationship between
the normalized sEMG signal vs. normalized force does
exist, and whether it is dependent on the exercise level
An sEMG
signal
Signal
digitization
Bandpass filter
10-500 Hz
Wavelet decomposition
Amplification
with gain
Feature extraction and
interpretation
Signal classification using
discrimination analysis
F i g. 1 Block diagram for the procedure of wavelet analysis of surface electromyogram signals.
Р и с. 1. Діаграма процедури вейвлет-аналізу сигналів поверхневої електроміограми.
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 4358
V. KARAN
and the rate of force production. First, the raw sEMG
signals for different muscle voluntary contractions
were acquired and processed using classical filters and
the wavelet transform approach. The processing of the
signal included the following steps:
(i) Filtering the signal with band-pass filters (10 Hz
and 500 Hz) updating the waveform graph cursors, to
represent current values of the upper and lower cut-off
frequencies.
(ii) Dual-channel spectral measurement on the
prefiltered and filtered signals, to determine the
frequency response of the filter.
(iii) Determination of different features (like
RMS, s.d., energy of the signal, integrated EMG, and
spectrogram). A front panel of the system is shown in
Fig. 2.
Second, band-pass filtering and discrete wavelet
transform (DWT) denoising of the sEMG signal were
done. Different Daubechies wavelet functions (db2
to db14) were utilized for the extraction of different
decomposition coefficients and for reconstruction of
the signal. Comparative data of raw sEMG signals
for three subjects with extracted features for different
muscular contraction forces are show in Table 1. To
describe the results of these wavelet features, various
representatives for denoised RMS are presented in
Table 2.
The raw sEMG signals were used to calculate the
F i g. 2. Labview-based code for feature extraction.
Р и с. 2. Labview-код для виділення ознак.
T a b l e 1. Feature sets for different movement intensities (low – high); dependence on the position on the biceps (S1 – S3)
Т а б л и ц я 1. Набори ознак для рухових феноменів різної інтенсивності; залежність від положення на біцепсі
Indices
low medium high
S1 S2 S3 S1 S2 S3 S1 S2 S3
RMS 0.08 0.10 0.14 0.32 0.28 0.31 0.59 0.54 0.47
MAV 0.06 0.07 0.10 0.23 0.20 0.23 0.41 0.36 0.31
VAR 0.004 0.007 0.018 0.09 0.07 0.09 0.34 0.27 0.21
s.d. 0.06 0.08 0.13 0.31 0.27 0.30 0.58 0.52 0.46
PSUM 0.008 0.014 0.013 0.147 0.116 0.141 0.607 0.426 0.257
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 4 359
WAVELET TRANSFORM-BASED CLASSIFICATION OF ELECTROMYOGRAM SIGNALS
RMS value, denoised power, and s.d. values for all
wavelet functions suitable for biomedical signal
processing with four levels of decomposition. Table
2 gives the results of collations of the average RMS
value, while Table 3 gives the average MdF values
of all chosen wavelet functions for three subjects at
various muscle contraction force levels.
According to the results shown in Table 2, wavelet
functions from the Daubechies family showed
satisfactory performances; it should be noted that the
wavelet function db4 showed a better performance
value than other wavelet functions. This means that
wavelet function db4 (from the average column in
Table 2) is capable of denoising sEMG signals better
than other wavelet functions of the same family.
Surface Myoelectric Signals. The RMS values
were computed for each signal and all force data files,
as this is the parameter that reflects more completely
physiological correlates of the behavior of motor units
during muscle contraction, and this has been termed
as a “gold” standard for analyzing surface EMG
signals. Since the wavelet function db4 shows the
better performance for the results, it was considered
the best for the EMG denoising process and further
feature extractions. In order to make a reliable signal
and muscle force determination, the knowledge on
the effects of time-varying factors on the mean (MnF)
and median (MdF) frequencies is very important.
Two time-varying factors, muscular force and muscle
geometry, are the major factors in the activities related
to dynamic muscle contractions (muscle force and/or
geometry are changing). The average values of the
MnF and MdF of the signal-force data from three
different subjects with different forces of muscle
contraction are displayed in Tables 3 and 4 These data
are an aggregate of all of the contractions performed
by all subjects during the experiment.
Now, during a sustained constant-force contraction,
the amplitude of the detected sEMG signal increases
as a function of time. In fact, this phenomenon of
myoelectric signal-force relationship approaches
towards linearity with a considerable confidence for
T a b l e 2. Average RMSs of the Db family for different force levels (three subjects)
Т а б л и ц я 2. Середні значення параметра RMS для сімейства Daubechies (дані трьох тестованих суб’єктів)
Indices low medium high average
Db2 0.0362 0.0674 0.0998 0.0678
Db3 0.0492 0.0659 0.1016 0.0722
Db4 0.0502 0.0664 0.1008 0.0725
Db5 0.0491 0.0664 0.1000 0.0418
Db6 0.0491 0.0665 0.0980 0.0715
Db7 0.0491 0.0660 0.0987 0.0712
Db8 0.0491 0.0.069 0.0999 0.0716
Db9 0.0498 0.0661 0.0997 0.0718
Db10 0.0492 0.0666 0.0985 0.0714
Db11 0.0491 0.0663 0.0982 0.0712
Db12 0.0490 0.0657 0.0991 0.0713
Db13 0.0491 0.0658 0.0996 0.0715
Db14 0.0492 0.0663 0.0991 0.0715
T a b l e 3. Average median frequencies (MdF)) for diferent force levels (three subjects)
Т а б л и ц я 3. Середні значення медіан частот (MdF) для різних рівнів зусиль (n = 3)
Values low medium high average
median frequency
(raw) 328.3 346.9 358.2 344.46
median frequency
(denoised) 142 226.4 288.4 218.93
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 4360
V. KARAN
the biceps brachii, i.e., increasing level in terms of the
MnF and MdF.
Linear Discrimination Analysis. A purpose of
discriminant function analysis is understanding
of the dataset resulting from the procedure; this
can give insight into the relationship between the
group membership and the variables used to predict
such membership. This approach has been used to
investigate independent variable mean differences
between the groups formed by the dependent variable
and also to determine the percent of variance in the
dependent variable explained by the independents
over and above the variance accounted for by control
variables, using sequential discriminant analysis.
Discriminant function analysis is broken into a
two-step process: (i) testing significance of a set of
discriminant functions, and (ii) classification.
First, the Wilks’ lambda is used to test if the
discriminant model as a whole is significant or not.
It is the ratio of within-group sums of squares to the
total sums of squares. This is the proportion of the
total variance in the discriminant scores not explained
by the differences among groups. Second, if the F test
shows the significance, the individual independent
variable is assessed to see if it differs significantly
from the group mean, and these are used to classify
the dependent variable. The lambda varies from 0 to
1, with 0 meaning that the group means differ from
each other, and 1 meaning that all group means are
the same. The associated significance value indicates
whether the difference is significant. In our case,
the Wilks’ lambda of 0.376 had a significant value
(P = 0.012); thus, the group means appear to differ
from each other. The associated χ2 statistic (6.354)
tests the hypothesis that the means of the functions
listed are equal across groups. The relatively small
significance value (P value) indicates that the
discriminant function does better than chance at
separating the groups. Since the value of P < 0.05, it
can be concluded that the model is a good fit for the
data significance.
Now, as we are interested in the relationship
between a group of the independent variables and one
categorical variable, it would be beneficial to know
how many dimensions we would need to express this
relationship. Using such relationship, we can predict
a classification based on the independent variables or
assess how well the independent variables separate
the classification categories. The larger the Eigen
value (0.376 in our study), the more the variance in
the dependent variable is explained by this function.
The canonical correlation (0.790) is the measure of
association between the discriminant function and
the dependent variable. The square of the canonical
correlation coefficient is the percentage of the
variance explained in the dependent variable. Finally,
the classification table (also called a prediction
matrix or table) used to assess the performance of
the model is shown in Table 5. It helps to describe a
simple summary of the number and percent of subjects
classified correctly and incorrectly.
Table 5 gives information about the actual group
membership. So, it is concluded that the overall
percentage of correct classification is 88.90%
T a b l e 4. Average mean frequencies(MnF) for different force levels
Т а б л и ц я 4. Усереднені значення середніх частот (MnF) для різних рівнів зусиль
Values low medium high average
mean frequency
(raw) 337.8 355.4 364.4 352.53
mean frequency
(denoised) 151.4 231.8 296.4 226.53
T a b l e 5. Linear discrimination analysis: classification results
Т а б л и ц я 5. Результати класифікації за допомогою лінійного дискримінантного аналізу
Values output
predicted group membership
total
0 1
Original
Count
0 6 0 6
1 1 2 3
%
0 100.0 0.0 100.0
1 33.3 66.7 100.0
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 4 361
WAVELET TRANSFORM-BASED CLASSIFICATION OF ELECTROMYOGRAM SIGNALS
Looking at the columns in Table 5 instead of the
rows, one can also calculate the positive probability
value:
(i) Positive probability value gives the confidence in
the predicted results. A higher probability means that
there is a high enough chance that a predicted model
will actually be significant (85.71%).
(ii) The specificity is the percentage of correct
classification predicted in the model (100.0%).
(iii) The sensitivity is the percentage of the model
correctly predicted (66.70%).
Finally, the best average classification result
calculated for the db4 wavelet family after applying
linear discrimination analysis is 88.90%, whereas
the worst classification result calculated for db5 is
77.80%.
Data Statistical Method. We were interested in
refining the experiment to increase its sensitivity for
detecting differences in the dependent variables. An
effective step to achieve better performance for the
classification of signals recorded at different voluntary
contractions is extraction of a feature from the raw data
before performing the analysis of multiple activities.
The analysis of extracted features further helps to
identify the significance of the sEMG-muscular force
relationship existing between these phenomena in
voluntary contractions.
To further extend the study of relat ional
interpretations of selected operations of the arm,
a statistic technique of analysis of variance of both
experimental and reconstructed data was implemented
for interpretation of the signal class separability in
order to identify the best sEMG signal amplitude
for different voluntary contractions optimum with
respect to establish the best myoelectric signal-
force relationship. So, to appreciate the classification
of the arm motions for multiple samples, one-way
analysis of variance with prior comparison has been
implemented. The analysis of variance (ANOVA) with
three independent groups related to the biceps and
presenting the raw, detailed, and approximated wavelet
coefficients is shown in Tables 6 and 7.
The basic procedure in this case is to derive two
different estimates of the variance from the data; then,
the ratio of these two estimates is calculated. One of
these estimates (SSB) is a measure of the effect of the
independent variable combined with the error variance,
while another estimate (SSW) characterizes the error
variance itself. Then, a significant F ratio among
two estimates is calculated. The significant F ratio
indicates that the population means are, probably, not
all equal to each other. Since the estimate of data for
the sum of squares between the groups (SSB, 0.2736,
0.2688) is large compared to the data for within the
group (SSW, 0.01, 0.0046). It is concluded that the
test statistic is significant at this level. The mean
T a b l e 6. Analysis of the variance results for biceps voluntary contractions (raw)
Т а б л и ц я 6. Результати аналізу варіанси для довільних скорочень біцепса (первинні дані)
Source of variation Sum of squares (dof) mean square Fisher ҆ s ratio (F) P critical value (fc)
Sum of between-group squares 0.273 2 0.212 82.08 0.0001 5.14
Sum of within-group squares 0.01 6 0.006
Total sum of squares 0.283 8
T a b l e 7. Analysis of the variance results for triceps voluntary contractions (raw)
Т а б л и ц я 7. Результати аналізу варіанси для довільних скорочень трицепса (первинні дані)
Source of variation Sum of squares (dof) mean square Fisher ҆ s ratio (F) P critical value (fc)
Sum of between-group squares 0.268 2 0.134 175.31 0.0001 5.14
Sum of within-group squares 0.004 6 0.0007
Total sum of squares 0.273 8
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 4362
V. KARAN
square error of sEMG signals helps to evaluate the
quality of the robustness function. The performance of
algorithms is the best when the mean square error has
the smallest value. Here, the mean square error (MSE)
values within the group are 0.0060 and 0.0007 for the
biceps and triceps muscles, respectively, which means
that the sEMG signal contains useful information, and
undesirable parts of the signals are removed.
The F ratio [18] is the statistic used to test the
hypothesis that the effects are real; in other words,
that the means significantly differ from each other.
There is a significant difference in the amplitude
gain across different motions, F (2, 6) = 82.086,
P < 0.05, F (2, 6) = 175.3 with the raw data for the
biceps and triceps muscles for three independent
voluntary contractions, respectively. From the Tables,
we can see that the F ratio is greater than the critical
value (fc) in all cases; so the means are significantly
different, and it is concluded that there is a more
significant difference between the groups (SSB) than
that within the groups (SSW). For both samplings of
the experimental data, analysis of variance revealed
the continuous significant differences over time,
which means that this technique is useful for revealing
differences in the shape and magnitude of sEMG signals
for independent motions. Thus, analysis of the variance
found statistical differences between electrode positions
(P < 0.05), between surface electrode conditions, and
for interaction between all groups.
DISCUSSION
Wavelet denoising was applied to ensure the
effectiveness of sEMG signals at various static
voluntary contractions of the elbow-controlling
muscles, since it provides better time and frequency
resolution simultaneously for the analysis of
nonstationary signals. During our study, five basic
parameters were extracted by analyzing the signal
amplitude corresponding to different voluntary
contractions, but the appropriate wavelet has been
chosen on the basis of the RMS and s.d. values. The
raw sEMG signals were used to calculate the RMS
and s.d. values for all wavelets suitable for signal
processing with four levels of decomposition.
Further analysis of variance (ANOVA) as a novel
approach for revealing differences in the shape and
magnitude of EMG signals for multiple motions is
implemented. The P values for biceps and triceps
F (2, 6) is 0.0001 that is much smaller than 0.05. So,
the null hypotheses of equal means is rejected, and,
finally, the test statistic is significant. The assessment
of the muscle force relation with sEMG signals can
be applied to a wide class of daily used applications.
Although the behavior of MnF and MdF is similar, the
value of the time domain mean frequency is slightly
greater than the time domain median frequency
because of the skewed shape of the sEMG power
spectrum. In addition, both mean and median features
can be considered universal indices to identify all
factors, including muscle geometry, muscular force,
and voluntary contraction. To summarize:
(i) The surface myoelectric signal-force relationship
is primarily determined by different muscle geometries
including electrode configuration, fibre diameter,
subcutaneous tissue thickness, etc.;
(ii) The electrode locations (interelectrode distance
of about 1 cm) over the muscle were changed during
the experiment;
(iii) The mean (MnF) and median (MdF) frequency
parameters are linear for the biceps and triceps brachii
muscles, with the amplitude of the myoelectric signal
increasing linearly with the force exhibited;
(iv) The one-way analysis of variance (ANOVA)
approach for comparing the ability of variations in
sEMG signals for maximum class separability has
been identified;
(v) The classification accuracy of 88.90% has been
achieved for upper limb class separability movements.
Acknowledgement. The author is grateful to Dr. Amod
Kumar and Dr. Ravinder Agarwal, PhD supervisors for helping
in writing this paper.
All procedures followed were in accordance with the
ethical standards of the responsible Committees on human
experimentation (institutional and national) and with the
Helsinki Declaration of 1975, as revised in 2000 (5). Written
informed consent was obtained from all subjects for being
included in the study.
The author, V. Karan, confirms the absense of any conflict
related to comercial or financial interests and to interrelations
with organizations or persons in any way involved in the
research.
В. Каран1
КЛАСИФІКАЦІЯ ЕЛЕКТРОМІОГРАФІЧНИХ СИГНАЛІВ
З ВИКОРИСТАННЯМ АНАЛІЗУ ANOVA, БАЗОВАНА НА
ВЕЙВЛЕТ-ПЕРЕТВОРЕННЯХ
1 Університет Тхапар, Патіала (Індія).
NEUROPHYSIOLOGY / НЕЙРОФИЗИОЛОГИЯ.—2015.—T. 47, № 4 363
WAVELET TRANSFORM-BASED CLASSIFICATION OF ELECTROMYOGRAM SIGNALS
Р е з ю м е
Досліджували можливість застосування вейвлет-аналізу
щодо сигналів поверхневої електроміограми (пЕМГ). Вико-
ристання видалення шумів із записів пЕМГ перед оброб-
кою таких сигналів для подальшого аналізу є дуже істотним.
Сигнали пЕМГ оцінювалися в наступній послідовності:
спочатку отриманий сигнал підлягав декомпозиції з ви-
користанням вейвлет-перетворення, потім декомпозовані
коефіцієнти аналізувались із застосуванням порогових ме-
тодик, і, нарешті, виконувалася реконструкція. Поперед-
ньо порівнювали ефективність видалення шумів у межах
вейвлет-сімейства Daubechies. Було встановлено, що вейв-
лет-перетворення db4 із цього сімейства виконує знешум-
лення найкращим чином. Отримані результати вказують на
те, що вейвлет-сімейства Daubechies є більш придатними
для аналізу пЕМГ сигналів, отриманих в умовах реєстрації
різних моторних реакцій м’язів верхніх кінцівок; досяга-
лася точність класифікації 88.9 %. Потім статистична ме-
тодика (однобічний повторний факторіальний аналіз) за-
стосовувалася щодо експериментальних коефіцієнтів для
встановлення якості розділення даних при різних рухах.
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