Backpropagation Artificial Neural Network To Detect Hyperthermic Seizures In Rats

Published Quarterly

Mangalore, S outh India

ISSN 0972-5997

Vol ume 1; Issue 4; October-December 2002

S hort Communication

Backpropagation Artificial Neural Network To Detect Hyperthermic
Seizures In Rats

Rakesh Kumar Sinha

School Of Biomedical Engineering, I nstitute Of Technology, Banaras Hindu University,

Varanasi (India) - 221005. E-mail: [email protected]

Corresponding Address: Rakesh Kumar S inha, C/O S ri D. P. S inha, Sector 5 ‘D’/2003,

Bokaro Steel City, Jharkhand (India) - 827006.Email: [email protected]

Citation: S inha RK, Backpropagation Artificial Neural Network To Detect Hyperthermic

Seizures In Rats. Online J Health Allied Scs. 2002; 4: 1

U R L : http: //www.ojhas.org/issue3/2002-4-1 .htm

Open Acces s Archive: http: //cogprints.ecs.soton.ac.uk/view/subjects/OJHAS .html

Abstract

A three-layered feed-forward back-propagation
Artificial Neural Network was used to classify the
seizure episodes in rats. S eizure patterns were
induced by subjecting anesthetized rats to a
Biological Oxygen Demand incubator at 45-47°C for
30 to 60 minutes. Selected fast Fourier transform
data of one second epochs of electroencephalogram
were used to train and test the network for the
classification of seizure and normal patterns. The
results indicate that the present network with the
architecture of 40-12-1 (input-hidden-output nodes)
agrees with manual scoring of seizure and normal
patter ns with a high recognition rate of 98.6% .

Keywords : Artificial Neural Networ k, fast Fourier
transform, electroencephalogram, Hyperthermic
seizures

I ntroduction

Heat stroke or hyperthermia is one of the most
serious of the disorders that may cause seizures .
Literatures suggest that continuous exposure to high
environmental heat as well as by hot water pour
over the head generate seizures in both man and
animals .(1 ,2) Several computer algorithms and
programs for automatic detection of epileptic
transients were developed but these methods were
found unable to recognize the exceptions and
minimize the number of false detections .
Alternatively, Artificial Neural Network (ANN) has
been successfully implemented for many pattern
classification problems including detection of
epileptic seizures.(3-5) However, most of the
previous ANN based methods use measures of the
electroencephalogram (EEG) such as amplitude,
width, slope and sharpness of series of consecutive
waves , measures thought to reflect in a general
sense what expert clinicians attempt during EEG
interpretation. In the present work, instead of using
the physical characteristics of EEG signals , fast
Fourier transform (FFT) has been used for the
training and testing of the ANN as it conveys more
information with respect to conventional analog EEG
r ecords . (6)

T he experiment was carried out on male Charles
Foster rats weighing 200-250 grams. Rats were
housed in the animal room that was artificially
illuminated with a 12 light cycle (7 .00 A.M. to 7 .00 P.
M.) and the ambient room temperature maintained
at 24± 1°C. Rats were anaesthetized with Urethane
anaesthesia (1 . 6gm/kg, I .P.) and three stainless
steel screw electrodes were aseptically fixed on the
rat’s head under stereotaxic guidance. Two
electrodes were placed on bilateral fronto-parietal
region and one grounding electrode at the anterior
most region of the skull to record the differential EEG
patterns. Anaesthetized rats after electrode
implantation were subjected to the thermal
environment in the Biological Oxygen Demand (BOD)
incubator with preset temperature at 45-47²C.(1)

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