J. Biomedical Science and Engineering, 2010, 3, 529-537
doi:10.4236/jbise.2010.35074 Published Online May 2010 (http://www.SciRP.org/journal/jbise/
JBiSE
).
Published Online May 2010 in SciRes. http://www.scirp.org/journal/jbise
Predicting heat-stressed EEG spectra by self-organising
feature map and learning vector quantizers
——SOFM and LVQ based stress prediction
Prabhat Kumar Upadhyay1, Rakesh Kumar Sinha2, Bhuwan Mohan Karan3
1Department of Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, India;
2Department of Biomedical Instrumentation, Birla Institute of Technology, Ranchi, India;
3Department of Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi, India.
Received 9 December 2009; revised 28 December 2009; accepted 30 December 2009.
ABSTRACT
Self-Organising Feature Map (SOFM) along with
learning vector quantizers (LVQ) have been designed
to identify the alterations in brain electrical poten-
tials due to exposure to high environmental heat in
rats. Three groups of rats were considered—acute
heat stressed, chronic heat stressed and control
groups. After long EEG recordings following heat
exposure, EEG data representing three different vi-
gilance states such as slow wave sleep (SWS), rapid
eye movement (REM) sleep and AWAKE were visu-
ally selected and further subdivided into 2 seconds
long epoch. In order to evaluate the performance of
artificial neural network (ANN) in recognizing
chronic and acute effects of heat stress with respect to
the control subjects, unsupervised learning algorithm
was applied on EEG data. Mean performance of
SOFM with quadratic taper function was found to be
better (chronic-92.6%, acute-93.2%) over the other
two tapers. The effect of LVQ after the initial SOFM
training seems explicit giving rise to considerable
improvements in performance in terms of selectivity
and sensitivity. The percentage increase in selectivity
with uniform taper function is maximum for chronic
and its control group (4.01%) and minimum for
acute group (1.29%) whereas, with Gaussian it is al-
most identical (chronic-2.57%, acute-2.03%, control-
2.33%). Quadratic taper function gives rise to an in-
crease of 2.41% for chronic, 1.96% for acute and
2.91% for control patterns.
Keywords: ANN; EEG; Heat Stress; SOFM; LVQ
1. INTRODUCTION
The scientific interest of stress in relevance to health and
disease began to develop in the 20th century, when Selye
started his work on stress with more suited scientific
analysis, and now stress has been accepted to be a state,
comprised of certain psychophysiological reactions that
prepare an organism for action. It is usually described to
be an essential component that is enabling the organism
to survive in the hostile environment and to make an
effort to compensate with the altered situation of the
stressful conditions. Stress has been defined as nonspe-
cific responses of the body to any demand. Though in
some respect, every demand made on the body is unique
and specific, but all stress, however, have one thing in
common; they increase the demand for the readjustment
for performance of adaptive functions, which reestablish
normally. Generally, stress is meant to be acute or at
least of a limited duration. The time limited nature of
this process renders its accompanying antianabolic,
catabolic and immunosuppressive effects temporarily
beneficial and of no adverse consequences. Chroni-
cally and excessiveness of stress system activation, on
the other hand, would lead to the syndromal state that
severe chronic disease of any etiology could present
with anorexia, loss of weight, depression, and peptic
ulcers.
Although, the problems of heat-afflicted illness are
receiving increased importance in view of the current
estimates of global warming and its impact on biological
systems, the etiological factors that lead to heat exhaus-
tion and heat stroke have not been well established.
However, the failure of cardiovascular system had been
thought to be an important factor. Inadequate acclimati-
zation also appears to be a significant factor predispos-
ing to the onset of heat stroke. Review of literature re-
vealed that the afflictions and damages to the central
nervous system (CNS) imposed by high environmental
temperature have largely been ignored as the likely
cause of heat induced mortality, although it is well
known that neurochemical and cellular mechanisms of
neural issues are highly temperature sensitive [1]. The
conventional long term paper recording of EEG signals

530 P. K. Upadhyay et al. / J. Biomedical Science and Engineering 3 (2010) 529-537
following stress events such as heat stress is not of much
diagnostic value. So, computer and digital signal process-
ing tools have been used to quantify the EEG signals for
all three sleep-wake stages after acute and chronic heat
stress. Since, long-term EEG recordings, in addition to
other two channels of electrophysiological signals, EOG
(Electrooculogram) and EMG (Electromyogram), reflect
the variations in sleep-wake states, so the changes in sleep
parameters following acute as well as chronic states were
also observed. Further, to reduce the labor involved in the
manual sleep staging and to analyze psychophysiological
alterations, artificial neural network’s architectures were
designed. Present study signifies the computerized recogni-
tion of different sleep-wake states and the changes occurred
in EEG signals due to exposure to high environmental heat.
In the last decade, several works introduced the use of
artificial neural network (ANN) as a tool for automated
sleep scoring. Most of the system used spectral in-
formation of the signal using Fourier transformation
[2]. Computerized EEG and other electrophysiological
parameters monitoring reduces the problem of huge data
handling. Computerization has led to more sophisticated
use of EEG, even in effective disorders, where percep-
tual processes are significantly distorted [3-5]. Fourier
transformation, as a conventional method, has been
widely used for the standard quantitative analysis of the
spectral decomposition and the clinical application of
EEG signals [6]. The ANN programs were developed for
the analysis of most of the works that rely on spectral
analysis and power spectrum method to evaluate elec-
trographic data. In an attempt to classify sleep-wake
stages determined, power of FFT or power spectrum
band were used for better performance of the system
[3,7]. The numerical values of the power of different
frequency bands were used as inputs to ANN. As multi-
layer perceptron neural network (MLPNN) undergoes
some limitations, the performance of SOFM has also
been tested to solve the problem at hand. In the present
study, an effort has been made to exploit the inherent
qualities of SOFM. The results obtained from computer
simulations have been found to be very encouraging.
In addition to widespread application of artificial
neural networks (ANNs) in diagnosis, much develop-
mental work is being undertaken in signal processing
and analysis of bioelectric signals. ANNs are widely
used as to process raw electroencephalogram (EEG) data
or features. Recently, ANNs have been employed suc-
cessfully for many pattern recognition problems of elec-
troencephalogram (EEG) spectral component [8-11],
K-complex detection [12-13], event related potentials
(ERPs) detection [14-15], classification of the evoked
potentials [16-17] and the recognition of epileptic spike
patterns [18-19].
Review of literature reveals that supervised ANNs
have been used many times for sleep-wake state identi-
fication, but the literature on the methodology for use of
unsupervised ANNs are still obscure. The SOFM as
proposed by T. Kohonen [20-21] follows unsupervised
learning (competitive learning) and consists of a single
layer feed-forward network or lattice, the neurons of
which become specifically tuned to various input pat-
terns through a self-organizing process. The spatial loca-
tion of a neuron then corresponds to a particular feature,
or group of features, of the input patterns. Output neu-
rons of a topographic map are usually arranged such that
each neuron has a set of neighbours. Each node of the
output layer is connected with all other nodes of the
same layer with inhibitory weights and competitive
learning occurs among all the nodes of the output layer.
The most highly activated node which has least Euclid-
ean distance becomes the winner for a particular pattern. It
has been realized that the trained and calibrated Self-Or-
ganising Feature Map (SOFM) alone can not be used as a
classifier. Hence, to increase the performance of the pattern
classifier, Learning Vector Quantizers (LVQ) is applied in
the trained SOFM [22]. In the present study, unsupervised
ANN system using both SOFM and LVQ has been de-
signed for classification of heat stressed spectra.
2. MATERIALS AND METHODS
2.1. Subjects and Electrode Implantation
The experiments were carried out with male Charles
Foster rats of age 12-14 weeks and weight around 180-
200 grams. The rats were individually housed in poly-
propylene cages (30 cm × 20 cm × 15 cm) with drinking
water and food (Hindustan Liver Limited, India) ad libi-
tum. All rats were kept in an ambient environment tem-
perature of 23 ± 1°C from birth and the animal room was
artificially illuminated with 12 : 12 hours Light : Dark
cycle, changed at 7 o’clock and 19 o’clock Indian Stan-
dard Time (IST). The technique of electrode implanta-
tion for polygraphic sleep recordings have been used as
suggested earlier [7].
2.2. Heat Stress Model
In order to produce the effects of heat stress, rats were
subjected to the Biological Oxygen Demand (BOD) in-
cubator at preset temperature of 38 ± 1℃ and relative
humidity 45-50%, simulated with the environmental
conditions of Varanasi (India) in the months of May and
June. For chronic heat stress, rats were subjected to the
incubator for one hour daily for 21 days of chronic heat
exposure from 8.00 a.m. to 9.00 a.m. and electrophysio-
logical signals were recorded on 22nd day whereas, acute
heat stressed rats were subjected to the incubator for
continuous four hours of heat exposure from 8.00 a.m. to
12.00 p.m. for a single day, just before the recording of
electrophysiological signals. Respective control groups
of rats were placed in the incubator at room temperature

P. K. Upadhyay et al. / J. Biomedical Science and Engineering 3 (2010) 529-537 531
(23 ± 1℃) and whole procedure was followed exactly
similar to that of their stressed groups.
2.3. EEG Data Acquisition
Four hours of continuous recordings of EEG, electro-
oculogram (EOG) and electromyogram (EMG) were
performed from 12 o’clock to 16 o’clock IST on the re-
cording day for chronic and acute heat stressed rats
through the 8 channels Electroencephalograph (EEG - 8,
Recorders & Medicare Systems, India). The paper re-
cordings were performed with standard amplifier setup
[23] and at the chart speed of 7.5 mm/sec. The digitized
data (at sampling frequency of 256 Hz) was collected,
stored and processed with the help of data acquisition
system (ADLiNK, 8112 HG, NuDAQ, Taiwan) and
processing software (Visual Lab.-M, Version 2.0 c,
Blue Pearl Laboratory, USA).
2.4. Self-Organising Feature Map and Learning
Vector Quantiser
With self-organised learning, no external teacher is re-
quired in order to adjust the weights of the ANN, but the
choice of the input data set will still reflect the general-
izing ability of the ANN (as for the supervised case). It
presents a method of identifying similarities (or features)
in a vast (unlabelled) training set. An important advan-
tage of self-organising ANNs over their supervised
counterparts is that they can be exposed to and make use
of vast quantities of input data for training purposes
without the need for assigning labels to each input for-
warded to the ANN. Let m, x, and α be the weight vector,
input vector and learning rate parameter respectively
then with the following rules the weights of the winning
neuron as well as its neighbours are updated.
)()];()([)()()1( tNifortwtxRttwtw ciiii 

)();()1( tNifortwtw cii 
where, Nc is a topological neighbourhood function
centered around the winning neuron c and Ri is the
neighbourhood taper function. Three different taper
function-uniform, Gaussian, and Quadratic have been
used for training the network, the mathematical expres-
sions of which are as followed:
Linear: Ri = 1, for i Nc, 
Gaussian: Ri = exp2
2
)1( 

c
i
N
d for i Nc,

Ri = 1-1.052
2
)1( 

c
i
N
d for i

Nc.
where, di is the Euclidean distance between the weight
vector wi and the winning weight vector wc. Learning rate (η)
and neighbourhood size (Nc) are assumed to be time vary-
ing. Two types of monotonically decreasing functions are
considered here, a piecewise linear decreasing function and
an exponential decreasing function. The piecewise linear
function is given by:
,);()1()( max
max
min
max
0kkif
k
k
k
k
k 



 
,;)( maxmin kkifk  
,;)1()( max
max
min
max
0kkif
k
k
N
k
k
NkNc 


.;)(maxmin kkifNkNc  
The piecewise exponential function is given by:
.;)(
,;)(
,;)(
,;)(
maxmin
maxmin
1
0
maxmin
maxmin
)1(
0
max
max
maxmax
kkifNkN
kkifNNkN
kkifk
kkifk
c
k
k
k
k
c
k
k
k
k




















where, in both cases, η0 and N0 give the initial learning
rate and neighborhood radius respectively, whereas ηmi n
and Nmin give the respective minimum values. Here, k
and µ denote iteration number and fraction of total
number of iteration respectively.
To achieve improved classification performance,
training and calibration of the SOFM with a supervised
learning scheme is followed. Learning vector quantization
(LVQ) is one such technique, developed by Kohonen
to fine-tune the weights of the trained SOFM in a
supervised manner [22]. LVQ is a supervised technique
that uses class information to move Voronoi vectors
slightly in such a way as to improve the quality of the
classifier decision regions. The initial values of wi before
the fine-tuning with LVQ, must be such that wi represent
the overall statistical density function of the input. The
SOFM is suited to achieve this. Out of the three versions
of the LVQ algorithm, LVQ1 has been applied in the
present work, which follows the following weight update
rules:
)]()()[()()1( twtxttwtw iii 




,
if i = c and x is classified correctly.
)]()()[()()1( twtxttwtw iii 




if i = c and x is classified correctly.
)]()()[()()1( twtxttwtw iii 




for i ≠ c.
2.5. Data Preparation and Simulation
The EEG data set prepared for input space of the net-
work was divided into training data set and test data set.
The training data set consists of the raw EEG signals of
SWS, REM and AWAKE states, each having matrix size
of [1, 512]. So, the total size of the input vectors for one

532 P. K. Upadhyay et al. / J. Biomedical Science and Engineering 3 (2010) 529-537
presentation at the input layer of SOFM is [1, 1536], in
which all the three sleep states have been equally pre-
sented. This constitutes one training epoch. The training
set was used to repeatedly train a 2-D lattice of neurons
of different sizes and varying SOFM training parameters.
The input data was presented to the network 600 times
with different values of learning rate parameter (η),
topological neighborhood function (N) centered around the
winning neuron and neighborhood taper function (h). The
weights of the trained SOFM now get ordered in the input
space such that they represent the underlying densities of
the inputs.
JBiSE
The program searches the position of the weight vectors
in relation to the input vectors after different iterations dur-
ing training SOFM of different sizes such as 4 × 4, 8 × 8, 10
× 10, 14 × 14. The distribution of the model vectors in the
n-dimensional space will approximate the probability dis-
tribution of the input vectors. The topographic organization
of the map will also approximate the metric ordering rela-
tions in the input space. Thus similar inputs project near
each other onto the map. The map thus forms an “elastic
surface” in the input space, which approximates the prob-
ability density function of the input samples. Increasing the
number of locations (neurons in the lattice) increases the
accuracy of the approximation. After training a 2-D lattice
of neurons by SOFM, each trained map was calibrated and
learning vector quantizer (LVQ) algorithm (a supervised
learning technique) was applied in each case for fine tuning
the weights of the trained SOFM.
2.6. Body Temperature
Core body temperature was recorded as stress markers for
both acute and chronic stress group of rats through the
thermistor probe connected to 6-channel telethermometer.
The marked probe at 4 cm was inserted to the rectum of the
animal and kept static for one minute to record the body
temperature. For acute stress group, body temperature was
recorded before and after the heat exposure. While for
the chronic stress group, the body temperature was re-
corded on every third day just before putting them into
the incubator for chronic heat stress.
3. RESULTS
With the processed EEG data sets as shown in Figure 1,
the network was simulated number of times and the per-
formance was calculated for some of the simulations
employing two decay functions of learning rate and
neighborhood size, three neighborhood tapering schemes,
and different number of training iterations. Use of linear
decay and exponential decay for learning rate and
neighborhood size did not show any fixed trend such
that conclusion could be drawn precisely. A few graphs
showing the mean change in Euclidian distance between
weight vectors of SOFM of varying sizes at successive
epochs during the training process, have been presented
in Figure 2. The first subscript denotes decay mode of
learning rate and neighborhood distance whereas, letters
1 and 2 stand for linear and exponential decay respec-
tively. The second subscript denotes taper function,
where 1, 2 and 3 represent linear, Gaussian, and quad-
ratic functions respectively. Time span of the ordering
phase and fine adjustment phase can distinctly be seen in
the curves of Figure 2. The ordering phase occurs within
the first 10%-20% of the training process and is char-
acterized by large changes in the Euclidean distances.
The fine adjustment phase is characterized by smaller
changes in the distances. Substantial increase in per-
formance was seen in the simulation of 8 × 8 SOFM in
acute heat stress. Number of iterations of the input vec-
tors required for simulation was set to 500 and 200 times
the number of neurons of the Kohonen layer. The results
as shown in the tables (Tables 1 and 2) suggest that iter-
ating 200 × size of the SOFM produces almost identical
Figure 1. Processed sleep EEG of awake, slow save sleep and REM.

P. K. Upadhyay et al. / J. Biomedical Science and Engineering 3 (2010) 529-537 533
020 40 60 80100120140 160 180 20
0
0
0.02
0
.
04
10
2,3
020 40 60 80100120140 160 180 20
0
0
0.02
0.04
6
2,3
020 40 60 80100120140 160 180 20
0
0
0.02
0.05
12
2,3
020 40 60 80100120140 160 180200
0
0. 02
0. 04
8
2,3
020 406080100 120 140 160 180200
0
0. 02
0. 04
14
2,2
020 40 6080100 120 140160 180200
0
0. 0 2
0. 0 4
4
2,2
020 406080100 120 140160 180200
0
0. 02
0. 04
8
1,1
020 406080100 120 140160 180200
0
0. 02
0. 04
8
2,2
020 4060 80100 120 140160 180200
0
0.02
0.04
10
2,2
Figure 2. The mean change in euclidean distance between weight vectors during training of SOFM of varying
sizes. The first subscript corresponds to decay mode of learning rate and neighbourhood distance whereas the
second subscript denotes the type of taper function.
results as iterating 500 × size of SOFM. In some simula-
tions, for both stress conditions, better network per-
formance has been obtained by iterating 200 × size of
the SOFM. For 14 × 14 SOFM used in chronic heat
stress, it was found to be 94.6% whereas, mean per-
formance of only 91.1% was obtained when the network
was iterated by 500 × size of the SOFM. All simulations
were performed on two different values of ηmin, where
comparatively, ηmin = 0.001 offered better performance.
As the SOFM size decreases, the mean changes in-
crease and the SOFM become more settled. 14 × 14
SOFM acquires the optimum set of weight vectors al-
most after 20% of the training whereas for 8 × 8 SOFM
it is 60%. It is also obvious that with the change of net-
work size, learning rate and neighborhood decay func-
tion, training time of the network as well as its conver-
gence are being directly affected. Having trained the-
network, calibration of SOFM is accomplished, in which
a class label has been assigned to each neuron ac cording
to the maximum voting criteria. Each neuron is also as-
signed a value, which provides the confidence level with
which each neuron represents that class. The boundaries
between the three patterns of heat stress-chronic, acute
and respective control groups (symbolized by-‘ch’, ‘ac’

534 P. K. Upadhyay et al. / J. Biomedical Science and Engineering 3 (2010) 529-537
Table 1. Mean performance of SOFM in chronic heat stress under varying conditions after simulation.
Table 2. Mean performance of SOFM in acute heat stress under varying conditions after simulation.
and ‘co’ respectively) as shown in Figures 3–5, are well
demarcated showing topological ordering of the neurons.
It is important to note that topological ordering is ob-
served, both in the plots of the weight vectors in the in-
put space and in the assigning of labels during calibra-
tion. Labels form neat clusters with easily distinguish-
able boundaries. It is at the boundary that the confidence
labels assigned to neurons are at the weakest, due to
overlapping of the input distributions. Another important
point is that the SOFM assigns equal number of neurons
to each class, if the three classes were equally repre-
sented in the training set. When a particular class is un-
der represented in the input set, it will also be under rep-
resented in the SOFM, because fewer weight vectors
will be assigned to represent that class of input data.
Results (Figure 3) also indicate that to some data set no
decision has been taken by the output layer nodes and
hence tie between the adjacent nodes seems to appear.
Parameter Mean performance (%) 8 × 8 SOFM
Decay Linear 90.1 Exponential 92.3 -
Iterations 500 × (SOFM Size) 91.4 200 × (SOFM Size) 92.3 -

min 0.01, 92.4 0.001, 92.8 -
Nc tapper Uniform 90.73 Gaussian 91.38 Quadratic 92.6
Algorithm SOFM 90.6 LVQ1 92.8 -
Parameter Mean performance (%) 10 × 10 SOFM
Decay Linear 80.2 Exponential 79.4 -
Iterations 500 × (SOFM Size) 83.2 200 × (SOFM Size) 83.0 -

min 0.01, 80.2 0.001, 79.1 -
Nc tapper Uniform 80.4 Gaussian 80.3 Quadratic 82.6
Algorithm SOFM 80.1 LVQ1 92.8 -
Parameter Mean performance (%) 14 × 14 SOFM
Decay Linear 93.1 Exponential 94.6 -
Iterations 500 × (SOFM Size) 91.1 200 × (SOFM Size) 94.6 -

min 0.01, 93.4 0.001, 93.7 -
Nc tapper Uniform 92.9 Gaussian 94.1 Quadratic 94.3
Algorithm SOFM 93.6 LVQ1 92.9 -
Parameter Mean performance (%) 8 × 8 SOFM
Decay Linear 84.4 Exponential 88.3 -
Iterations 500 × (SOFM Size) 88.1 200 × (SOFM Size) 87.8 -

min 0.01, 91.3 0.001, 92.45 -
Nc tapper Uniform 90.73 Gaussian 92.3 Quadratic 93.2
Algorithm SOFM 90.8 LVQ1 93.6 -
Parameter Mean performance (%) 10 × 10 SOFM
Decay Linear 91.5 Exponential 91.7 -
Iterations 500 × (SOFM Size) 91.0 200 × (SOFM Size) 92.3 -

min 0.01, 92.4 0.001, 92.2 -
Nc tapper Uniform 91.1 Gaussian 91.5 Quadratic 90.6
Algorithm SOFM 91.4 LVQ1 92.8 -
Parameter Mean performance (%) 14 × 14 SOFM
Decay Linear 94.1 Exponential 94.8 -
Iterations 500 × (SOFM Size) 93.1 200 × (SOFM Size) 94.2 -

min 0.01, 93.6 0.001, 92.1 -
Nc tapper Uniform 91.8 Gaussian 92.1 Quadratic 94.3
Algorithm SOFM 90.7 LVQ1 93.5 -

P. K. Upadhyay et al. / J. Biomedical Science and Engineering 3 (2010) 529-537 535
cr
cr
cr
co
co
co
co
co
cr
cr
co
co
co
co
co
co
cr
cr
cr
ac
co
co
co
co
cr
cr
ac
ac
co
co
co
cr
cr
ac
ac
co
co
co
cr
cr
ac
ac
ac
ac
ac
cr
cr
cr
ac
ac
ac
ac
ac
cr
cr
cr
ac
ac
ac
ac
ac
crcr
co
co
Figure 3. Topological ordering of the output neurons of 8 × 8 SOFM
representing three patterns of heat stress-chronic, acute, and control (rep-
resented by class labels-cr, ac and co respectively).
cr
co
co
co
cr
co
co
cr
ac
ac
ac
cr
ac
ac
ac
cr
Figure 4. Topological ordering of the output neurons of 4 × 4 SOFM
representing three patterns of heat stress-chronic, acute, and control (rep-
resented by class labels-cr, ac and co respectively).
cr
cr
cr
cr
co
co
co
co
co
co
cr
cr
cr
cr
co
co
co
co
co
co
cr
cr
cr
cr
co
co
co
co
cr
cr
cr
cr
cr
co
co co
co
co
cr
cr
cr
ac
ac
co
co
co
cr
cr
cr
ac
ac
ac
ac
co
co
co
cr
cr
cr
ac
ac
co
co
co
ac
cr
cr
ac
ac
ac
ac
ac
co
ac
cr
cr
cr
ac
ac
ac
ac
ac
ac
ac
cr
cr
cr
ac
ac
ac
ac
ac
ac
ac
coco co
co
ac
acac
Figure 5. Topological ordering of the output neurons of 10 × 10 SOFM
representing three patterns of heat stress-chronic, acute, and control (rep-
resented by class labels-cr, ac and co respectively).

536 P. K. Upadhyay et al. / J. Biomedical Science and Engineering 3 (2010) 529-537
Depending on the individual performance of several
SOFM networks, 10 × 10 SOFM was selected and
trained for recognizing three clusters. In heat stress de-
tection, using 10 × 10 SOFM, average selectivity table
(Table 3) confirms the presence of three separate pat-
terns-chronic, acute and control groups. For acute and
control patterns, Gaussian taper function finds advantage
over the other two functions. Again training of SOFM
followed by fine-tuning with LVQ increases the per-
formance. This percentage increase in selectivity with
uniform taper function is maximum for chronic and its
control group (4.01%) and minimum for acute group
(1.29%) whereas, with Gaussian it is almost identical
(chronic-2.57%, acute-2.03%, control-2.33%). Quadratic
taper function gives rise to an increase of 2.41% for
chronic, 1.96% for acute and 2.91% for control patterns.
The study of average sensitivity of 10 × 10 SOFM (Ta-
ble 4) ascertains the appearance of three distinct patterns
in which all three taper functions were found to offer
identical results. However, small variation in the average
sensitivity can be realized for LVQ.
4. DISCUSSION
The changes in body temperature of the present rat mod-
el of heat stress due of acute or chronic heat stress is the
sterotype phenomena of heat stress and thus confirms the
stressful events along with EEG variations in these ex-
Table 3. Average selectivity of 10 × 10 SOFM for heat stress
detection.
Table 4. Average sensitivity of 10 × 10 SOFM for heat stress
detection.
periments. Special features provided by unsupervised
networks of labeling large quantities of data and training
them in a self-organized manner have been successfully
utilized in the present work. Since a number of training
and testing parameters play important roles in the clus-
tering problems, the effect of the various parameters on
the training of a SOFM has been studied by performing
simulation in Matlab. To achieve better classification
result, each parameter was varied one by one during si-
mulation and has been tabulated. Performance of differ-
ent SOFM was finally shown in terms of selectivity and
sensitivity. SOFM and LVQ were primarily used for de-
tecting three different patterns-chronic, acute, and con-
trol. EEG data of control subjects of chronic group and
acute group have been mixed together for training, as
variation with respect to stressed subjects were not found
to be much. Each simulation was performed on a differ-
ent input data set. After SOFM training was complete,
LVQ was used to calibrate SOFM.
Results obtained from the mean changes in Euclidian
distance between weight vectors during training of
SOFM of varying size and varying parameters indicated
the time of commencement of ordering phase and fine
tuning phase. The importance of appropriate selection of
taper functions and decay functions has also been well
studied by comparative analysis. After the LVQ was ap-
plied, all nodes in the output layer were found to be so
tuned that three classes of heat stress become apparently
separated. Observations suggest that the reason behind
the largest change encountered in performance of many
simulations, might have been due to the use of different
neighborhood taper functions. Overall, performance was
found to be better for quadratic taper over the other two
tapers. In some simulations, it was witnessed that the
increase in performance due to LVQ was negligible,
which might have occurred owing to the fact that the
input clusters were already reasonably well defined and
with a minimal overlap.
In the present work, an attempt has been made to
classify stressful conditions by means of changes in
EEG signals induced by high environmental tempera-
tures. The review of literature suggests that no work
has been reported that classifies heat stressed condi-
tions from normal candidates with help of SOFM and
LVQ. The ANN provides reliable information about
the stressed and normal artifact-free EEG power spec-
tra. However, in practical applications, EEG artifacts
can influence the sensitivity of the network. EEG pat-
tern recognition by ANN and clinical skill, are, how-
ever, not mutually exclusive but even reinforce each
other, and it is believed that a human clinician must
remain a necessary component of computerized diag-
nostic procedures to ensure a significant, high level of
diagnostic validity [24].
Uniform taper
Chronic Acute Control
SOFM 88.42 88.91 87.86
LVQ1 92.43 90.2 91.64
Gaussian taper
SOFM 87.62 89.50 88.02
LVQ1 90.19 91.53 90.35
Quadratic taper
SOFM 87.09 88.87 87.09
LVQ1 89.23 90.83 90
Uniform taper
Chronic Acute Control
SOFM 88.30 87.54 87.81
LVQ1 91.16 90.70 89.80
Gaussian taper
SOFM 87.64 88.70 87.64
LVQ1 89.97 90.65 90.23
Quadratic taper
SOFM 86.89 88.58 86.98
LVQ1 89.25 91.06 89.95

P. K. Upadhyay et al. / J. Biomedical Science and Engineering 3 (2010) 529-537 537
Studies indicate that, usually only 1-5% of the EEG
record is in clinical interest [25]; neural networks can
become useful for the on-line classification of EEG
waves. Since, exposure to high environmental heat has
significant effects on brain signal, SOFM with LVQ can
be used efficiently to identify the changes in brain sig-
nals, occurred due the stressful events and can also be
used further to develop an automated detection system
for psychophysiological analysis. Although, in the pre-
sent study, on-line classification was not carried out, it
may be possible with the help of fast computers and spe-
cific software. Furthermore, EEG technicians can easily
be trained for the manual selection of already detected
events whereas; recognition of abnormal patterns in the
background of the ongoing EEG requires substantial
experience.
JBiSE
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