This document presents a comparative analysis of healthy and neuropathic EMG signals using power spectral density (PSD). The study analyzes EMG signals from healthy muscles and those affected by neuropathy. It uses Welch's PSD estimation method with Hamming and Kaiser windows to compare the distribution of power over frequency components between the two signal types. The analysis found that neuropathic signals had a broader distribution over frequencies compared to healthy signals. This type of spectral analysis could help differentiate between normal and neuropathic muscle conditions for diagnosing neuropathies.

1. A Comparative Analysis of Neuropathic and
Healthy EMG Signal Using PSD
Akash Kumar Bhoi, Karma Sonam Sherpa, Pradeep Kumar Mallick
Abstract- The presented approach is conceptually useful in
illustrating the alteration in motor units (MUs) for
neuromuscular disorders and discussed on the properties of
PDS & PSD of EMG signals. The proposed power spectral
density method comparatively analyzed the healthy &
neuropathy signals with Welch's PSD estimation method by
Hamming & Kaiser Window. The distributions of power over
frequency components for both the signals are significantly
compared. This analysis is intended to provide an automatic
diagnosis of an individual’s muscle condition.
Index Terms- Biomedical signal processing,
Electromyography, Multi spectral imaging, Diseases.
I. INTRODUCTION
Neuropathies describe damage to the peripheral nervous
system which transmits information from the brain and
spinal cord to every other parts of the body. More than 100
types of neuropathies have been identified. The impaired
function and symptoms depend on the types of nerves
(motor, sensory, or autonomic) that are damaged [30]. The
muscle membrane potential is of about –90 mV [29].
Measured EMG potentials range between less than 50 μV
and up to 20 to 30 mV, depending on the muscle under
observation. Typical repetition rate of muscle motor unit
firing is about 7–20 Hz, depending on the size of the muscle,
previous axonal damage and other factors. Damage to motor
units can be expected at ranges between 450 and 780 mV.
Clinical electromyography analyses the electromyogram
(EMG) recorded from a contracting muscle using a needle
electrode to diagnose neuromuscular disorders. EMG is
composed of discrete waveforms called motor unit action
potentials (MUAPs), which result from the repetitive
discharges of groups of muscle fibers called motor units
(MUs) [1].
Akash Kumar Bhoi is with the Applied Electronics & Instrumentation
Engineering Department, Sikkim Manipal Institute of Technology (SMIT),
India (email: akash730@gmail.com)
Karma Sonam Sherpa is with the Electrical & Electronics Engineering
Department, Sikkim Manipal Institute of Technology (SMIT), India (email:
karmasherpa23@gmail.com)
Pradeep Kumar Mallick is with the Department of I&CT, F.M.
University, India (pradeepmallick84@gmail.com)
The term MU refers collectively to one motoneuron and
the group of muscle fibers it innervates and is the smallest
unit of skeletal muscle that can be activated by volitional
effort. MUAPs from different MUs tend to have distinct
shapes, which remain almost the same for each discharge.
The MUAPs can therefore be identified and tracked using
pattern recognition techniques. The resulting information
can be used to determine the origin of the weakness, i.e.
neurogenic or neuropathy diseases [1, 3]. Subjective MUAP
assessment, although satisfactory for the detection of
unequivocal abnormalities, may not be sufficient to
delineate less obvious deviations or mixed patterns of
abnormalities [4].
MUAPs from different MUs tend to have distinct shapes,
which remain almost the same for each discharge. These
MUAPs can be identified and tracked using different pattern
recognition techniques. The resulting information can be
used to determine the neuromuscular diseases [1-3].
Fuglsang-Frederiksen and his group developed a rule-based
EMG expert system named KANDID [11], [12] and
Jamieson [13], [14] developed an EMG processing system
based on augmented transition networks. In most of these
systems, the generation of the input pattern assumes a
probabilistic model, with the matching score representing
the likelihood that the input pattern was generated from the
underlying class [15]. Pattichis et al gave a series research
yield of classifying MUAPs for differentiation of motor
neuron diseases and myopathies from normal [16]. In [21],
nerve conduction studies in axonopathies and demyelinating
neuropathies is presented and clinical electromyopathy
described in [23]. Application of PSD in practical
electromyopathy will be very crucial [22].
Various classification systems for differentiation of
neuromuscular disorders were introduced, applying mainly
neural networks [25] and support vector machines (SVM)
[26]. Rok Istenic et al. presented Statistical and Entropy
Metrics on Surface EMG for neuromuscular disorders
analysis [27].In [28], diagnosis of neuromuscular diseases
by using PCA and PNN was described. Two prominent
areas; first: the pre-processing method for eliminating
possible artefacts via appropriate preparation at the time of
recording EMG signals, and second: a brief explanation of
the different methods for processing and classifying EMG
signals is reviewed in [29].
There are significant differences in the characteristics and
treatment of disorders of muscle cells (myopathy) and
nerve damage in PNS (neuropathy). Spectral analysis of
neuropathic signals by PSD provides information on
International Conference on Communication and Signal Processing, April 3-5, 2014, India
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2. distribution of power over frequency which is different in
case of healthy EMG signal.
A. PROPERTIES OF THE POWER DENSITY
SPECTRUM FOR EMG SIGNAL
The power density spectrum of the EMG signal may be
formed by summing all the auto and cross-spectra of the
individual MUAPTs, as indicated in this expression:
Si() + Suiuj(
,
) (1)
where, Su, ( ) = the power density of the MU APT, Ui
(t); and S () = the cross-power density spectrum of
MUAPTs Ui(t) and u;(t). This spectrum will be nonzero if
the firing rates of any two active motor units are correlated.
Finally, p = the total number of MUAPTs that comprise the
signal; q = the number of MUAPTs with correlated
discharges. For details of this mathematical approach, refer
to De Luca and Van Dyk (1975). De Luca et al (1982b)
have shown that many of the concurrently active motor
units have, during an isometric muscle contraction, firing
rates which are greatly correlated. It is not yet possible to
state that all concurrently active motor units are correlated.
Therefore, q is not necessarily equal to p, which represents
the total number of MUAPTs in the EMG signal. [1][8][10]
Sm(, t, F) = R(, d)[∑ Sui(, t)( )
+
∑ Suiuj(, t) (2)
( )
,
where, MUAPT power density function (, , )
There are three eventualities that may influence its time
dependency: (1) the characteristics of the shape of the
MUAP Ui(t) and Uj(t) change as a function of time; (2) the
number of MUAPTs which are correlated varies as a
function of time; (3) the degree of cross-correlation among
the correlated MUAPTs varies. A change in the shape of
the MUAP of Ui(t) and Uj(t) would not only cause an
alteration in the cross-power density term but also would
cause a more pronounced modification in the respective
auto power density spectra. Hence, the power density
spectrum of the EMG signal would be altered regardless of
the modifications of the individual cross-power density
spectra of the MUAPTs.
There is to date no direct evidence to support the other
two points. In fact, De Luca et al (1982a and b) have
presented data which indicate that the cross-correlation of
the firing rates of the concurrently active motor units does
not appear to depend on either time during, or force of a
contraction [1][4]. This apparent lack of time-dependent
cross-correlation of the firing rates is not inconsistent with
previously mentioned observations, indicating that the
synchronization of the motor unit discharges tends to
increase with contraction time [6][24].
II. SPECTRAL ANALYSIS OF EMG SIGNAL
Spectrum analysis is also applied to EMG studies.
Various feature extraction methods based on the spectral
analysis are experimented. By using of information
contained in frequency domain could lead to a better
solution for encoding the EMG signal. Time-frequency
analysis, based on short-time Fourier transform is a form of
local Fourier analysis that treats time and frequency
simultaneously and systematically. The characters of EMG
signals in frequency domain are explored and demonstrated
in this paper. The short time variability of spectrum, which
is an essential fact for using time-frequency methods in
EMG feature extraction, is also discussed in this paper. The
analysis can provide important clues to design feature
extraction methods. Wavelets approach is another powerful
technique for time-frequency analysis [24].
III. POWER SPECTRAL DENSITY (PSD) OF EMG
SIGNAL
EMG Signals cannot be described by a well-defined
formula. The distributions for the various grasp types can be
however described with the probability laws. EMG signal is
a random process whose value at each time is a random
variable [7]. The Fourier transform used in the previous
section views non random signals as weighted integral of
sinusoidal functions. Since a sample function of random
process can be viewed as being selected from an ensemble
of allowable time functions, the weighting function for a
random process must refer in some way to the average rate
of change of the ensemble of allowable time functions. The
power spectral density (PSD) of a wide sense stationary
random process X (t) is computed from the Fourier
transform of the autocorrelation function R(τ ) :
Sx(f) = R().
∞
∞
e 
d (3)
where, the autocorrelation function
R() = E[X(t + )X(t) (4)
The nonparametric methods are methods in which the
estimate of PSD is made directly from a signal itself. One
type of such methods is called periodogram. The
periodogram estimate for PSD for discrete time sequence x1,
x2, x3 …. xk is defined as square magnitude of the Fourier
transform of data:
(% ) =
1
.  Xm . e 
² (5)
An improved nonparametric estimator of the PSD is
proposed by Welch P.D. The method consists of dividing the
time series data into (possibly overlapping) segments,
computing a modified (windowed) periodogram of each
segment, and then averaging the PSD estimates. The result is
Welch's PSD estimate. The multi taper method (MTM) is
also a nonparametric PSD estimation technique which uses
multiple orthogonal windows [24].
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3. IV. RESULTS
Standard physionet signals (Healthy & Neuropathy) of
10sec length are recorded for the analysis purpose.
This method of computing probability plots with normal
distribution for healthy & neuropathy data are in fact to
estimates the location and scale parameters for normal and
abnormal data points in order to differentiate two different
signals.
Fig.3. Probability plot for healthy & neuropathy signals
a. Hamming Window
The coefficients of a Hamming window are computed
from the following equation.
ω(n) = 0.54 − 0.46 cos 2π , 0 ≤ n ≤ N (6)
The window length is = + 1
b. Kaiser Window
The default value for beta is 0.5.To obtain a Kaiser
window that designs an FIR filter with side lobe attenuation
of α dB, use the following β.
 =
0.1102( − 21),  > 50
0.5842( − 21) .
+ 0.07886( − 21), 5 50 ≥  ≥ 21 (7)
0,  < 21
Increasing beta widens the main lobe and decreases the
amplitude of the side lobes (i.e., increases the attenuation).
During a sustained isometric contraction the surface EMG
signal becomes “slower”, the power spectral density is
compressed toward lower frequencies and spectral variables
(MNF, MDF) decrease. The decrease of these variables
reflects a decrease of muscle fiber conduction velocity and
changes of other variables (such as active motor unit pool,
degree of synchronization, etc) [24].
fm = f P(f)df/ P(f)d (8)
Fig.1. The input healthy
EMG signal
Fig.2.The input neuropathy
EMG signal
Fig.4. Power spectral density
of normal EMG signal with
hamming window
Fig.5. Power spectral density
of neuropathy EMG signal
with hamming window
Fig.8.Power spectral density
of normal EMG signal with
kaiser window
Fig.9.Power spectral density of
neuropathy EMG signal with
kaiser window
Fig.6. Periodogram power
spectral density estimate of
normal EMG signal with
hamming window
Fig.7. Periodogram power
spectral density estimate of
neuropathy EMG signal with
hamming window
Fig.10. Periodogram power
spectral density estimate of
normal EMG signal with kaiser
window
Fig.11. Periodogram power
spectral density estimate of
neuropathy EMG signal with
kaiser window
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4. P(f)df = P(f)df =
1
2
P(f)df (9)
Fig12. Mean and median spectral frequencies of the EMG signal (MNF and
MDF) [24]
The PSD by Welch estimation method with Hamming &
Kaiser Window shown (Fig.4 to Fig.11) above summarizes
the distribution of frequency components over power for the
entire length of the EMG data. The variation of frequency
can be visualised for healthy and neuropathy cases. The
comparative analysis of frequency components between
normal and neuropathy muscle signals is seen in the
peridogram mean-square spectrum estimate, where the
neuropathy signal (Fig.14) is scattered over the frequency
range of -400Hz-400Hz & the healthy signal (Fig.13)
present in the mid range of -100Hz-100Hz. This analysis can
be helpful in classification of normal and neuromyopathic
EMG signals. Qualitative assessments can be made by
calculating the PSD for each segment of data and comparing
them.
V. CONCLUSION
The methodology described in this work make possible
the development of a fully automatic electromyogram
(EMG) signal analysis which is accurate, simple, fast and
reliable enough to be used in routine clinical environment. It
has been shown that the mean and median frequencies of the
EMG signal decrease with time during a task that induces
fatigue. The result essentially gives an evaluation for
contribution of each frequency to the original signal. In
order to gain meaningful information from this type of
calculation, the segment of data being studied must be
stationary, meaning that the statistics of the signal do not
change with time. Success in this task would be a significant
medical breakthrough. The most important application of
spectral analysis in this study was to make differentiate
between normal and neuropathy EMG signals with their
spectral behaviour. This analysis leads to better investigation
of neuropathy diseases and the origin of such diseases. Still
the diagnostic results could be further investigated in future
works with larger data set and other feature sets.
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