Backpropagation Artificial Neural Network To Detect Hyperthermic Seizures In Rats

Discussion

In the present work, an approach of detection of
hyperthermia induced seizure and normal EEG
patterns through ANN has been successfully
implemented and experimentally tested. Features
calculated from the FFT such as relative power in
various frequency bands and then using an ANN to
generate a single number that indicates the degree
of which the event is a seizure (3, 12) was used
previously to classify seizure patterns. Instead of the
features from the FFT of the EEG signals, in the
present work, the selected frequency band of digital
values of the FFT from one second epochs of the EEG
signals for the training and testing of the ANN were
used. The EEG spike patterns represent very good
agreement with the human manual scoring.(3) The
performance of the detector was observed with
moderately high recognition rate of 98.6% in
recognizing normal and seizure patterns. The results
suggest that ANN is capable of clustering the input
information with greater reliability similar as shown
by Hopfield and Tank (13) and these analyses can
substantially increase the power of analysis. Once
the ANN is trained, the converged weights were
stored and re-used to obtain instantly the result of
seizure detection. The accuracy of recognition
however, was found sensitive to several parameters
such as the recording environment, the type of
signals used, sample size, training method, the
choice of network model and preprocessing of
signals. Although in this work, online seizure
detection has not been done, which may be possible
with the help of fast computer and dedicated
software.

The advantages and disadvantages of ANN in the
clinical diagnosis have not been extensively explored
yet. However, by application of these results, the
future scope can be outlined. The ANN can be useful
in differential diagnosis because the network can be
trained with large data sets derived from patients
with clear-cut, but clinically different diseases. Since
only 1-5% of long term recording of EEG signals are
of interest in clinical diagnosis,(3) the ANN can
become useful for online monitoring of pathological
events. Furthermore, the technicians can easily be
trained for the manual selection of the already
detected events, whereas recognition of abnormal
patterns in the background of ongoing EEG requires
substantial                                experience.

The author is grateful to Dr. Amit Kumar Ray,
Reader, School of Biomedical Engineering, Institute
of technology, Banaras Hindu University, Varanasi
(India) for providing necessary facilities for EEG data
collection and processing for the experiment.

References

1. Morimoto T, Nagao H, Sano N, et al.
Electroencephalographic study of rat hyperthermic
seizures. Epilepsia. 1991;32(3):289-93.

2. Ullal GR, Satishchandra P, Shankar SK.
Hyperthermic seizures: an animal model for hot
water epilepsy. Seizure. 1996;5(3):221-28.

3.   Jando G, Seigel RM, Horva th Z et al. Pattern

recognition of the electroencephalogram by
artificial neural networks. Electroencephal clin
Neurophysiol. 1993; 86: 100-9.

4.   Webber WRS, Lesser RP, Richardson RT et al. An

approach to seizure detection using an artificial
neural network. Electroencephal clin Neurophysiol.
1996; 98:250-72.

5.   Gabor AJ, Leach RR, Dowla FU. Automated seizure

detection using a self organizing neural network.
Electroencephal clin Neurophysiol. 1996; 99:257-
66.

6.   Sarbadhikari SN. A Neural network confirms that

physical exercise reverses EEG changes in
depressed rats. Med Engg & Phy. 1995; 17(8):
579-82.

7.   Sarbadhikari SN, Dey S, Ray AK. Chronic exercise

alters EEG power spectra in an animal model of
depression. Indian J Physiol Pharmacol. 1996;
40(1):47-57.

8.   Goel V, Brambrink AM, Baykal A et al. Dominant

frequency analysis of EEG reveals brain’s response
during injury and recovery. IEEE Trans Biomed
Engg. 1996; 43(11):1083-92.

9. Rao V, Rao, H. C++ Neural networks and fuzzy
logic. First Edition. New Delhi: BPB Publications;
1996. p. 123-76.

10. Freeman JA, Skapura, DM. Neural Networks:
Algorithms,   Applications   and Programming

Techniques. Addison Wesley: First ISE reprint;
1999.

11. Rumelhart DE, Hinton GE, Williams RJ. Learning
representations by   back-propagating  errors.

Nature. 1986; 323:533-36.

12. Sharma A, Wilson SE, Roy R. EEG classification for
estimating anesthetic depth during halothane
anesthesia. In Proceedings of 14^th annual
international conference IEEE Engineering in
Medicine and Biology Society, New York. 1992. p.
2409-10.

13. Hopfield JJ, Tank DW. Computing with neural
circuit: a model. S cience. 1 986; 223: 625-33.

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