Backpropagation Artificial Neural Network To Detect Hyperthermic Seizures In Rats

Sinha, Mr Rakesh Kumar (2002) Backpropagation Artificial Neural Network To Detect Hyperthermic Seizures In Rats. [Journal (On-line/Unpaginated)]

Full text available as:



A three-layered feed-forward back-propagation Artificial Neural Network was used to classify the seizure episodes in rats. Seizure patterns were induced by subjecting anesthetized rats to a Biological Oxygen Demand incubator at 45-47ºC 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 patterns with a high recognition rate of 98.6%.

Item Type:Journal (On-line/Unpaginated)
Keywords:Artificial Neural Network, fast Fourier transform, electroencephalogram, Hyperthermic seizures
Subjects:JOURNALS > Online Journal of Health and Allied Sciences
ID Code:3226
Deposited By:Kakkilaya Bevinje, Dr. Srinivas
Deposited On:17 Oct 2003
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

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. Jandó G, Seigel RM, Horvà 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 14th 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. Science. 1986; 223:625-33.


Repository Staff Only: item control page