International Journal of Computer
& Organization Trends

Research Article | Open Access | Download PDF

Volume 1 | Issue 3 | Year 2011 | Article Id. IJCOT-V1I3P307 | DOI : https://doi.org/10.14445/22492593/IJCOT-V1I3P307

Performance Evaluation of Different Neural Networks Used for Seizure Detection


Miss Ashwini D. Bhople, Prof. P. A. Tijare

Citation :

Miss Ashwini D. Bhople, Prof. P. A. Tijare, "Performance Evaluation of Different Neural Networks Used for Seizure Detection," International Journal of Computer & Organization Trends (IJCOT), vol. 1, no. 3, pp. 35-39, 2011. Crossref, https://doi.org/10.14445/22492593/IJCOT-V1I3P307

Abstract

Brain is one of the most important and complicated organs of humans. It is also susceptible to degenerative disorder, such as epilepsy. A disease caused due to temporary alternation in brain function due to abnormal electrical activity of a gro up of brain cells and is termed as epileptic seizure. It is a common chronic ne urological disorder characterized by seizures . In epilepsy, the normal pattern of neuronal activity becomes disturbed, causing strange sensations, emotions, and behavior or sometimes convuls ions, muscle spasms, and loss of consciousness. The term ‘Epilepsy’ is derived from the Greek word epilambanein or epilepsía which means ‘to seize or attack’. Anything that disturbs the normal pattern of brain cells (neuron) activity from illness to brain damage to abnormal brain development can lead to seizures. Epilepsy may develop because of an abnormality in brain wiring, an imbalance of nerve signaling chemicals called neurotransmitters, or some combination of these factors. Having a seizure does not n ecessarily mean that a person has epilepsy. Only when a person has two or more seizures is he or she considered to have epilepsy. The seizure occurs at random to impair the normal function of the brain. Seizures can be classified into two main categories d epending on the extent of involvement of various brain regions focal (or partial) and generalized. Generalized seizures involve from a circumscribed region of the brain, often called epileptic foci. EEGs and brain scans are most common and cost effective d iagnostic test for epilepsy. Worldwide, epilepsy affects 50 million people. In this paper different neural networks are studied and compared, for the detection of seizure

Keywords

Support Vector machine (SVM), Elman network (EN) , probabilistic Neural Ne twork (PNN), Generalized Feed Forward (GFFNN), Back Propagation Neural Network (BPNN) and Multilayer perceptron (MLP)

References

[1]. R. Harikumar and B. Sabarikumar narayanan ‖Fuzzy techniques for classification of epilepsy risk from EEG signal‖, IEEE Conference on convergent technology for AsiaPacific region TENCON 2003.
[2]. R. Harikumar and Dr. S. Raghavan and Dr. (Mrs.) R. Sukanesh ―Genetic Algorithm for classification of Epilepsy risk level from EEG signals‖, IEEE Conference on signals, Systems and Computers 2004.
[3]. J. Gotman, ―Automatic recognization of epileptic seizures in the EEG, Electroencephalogram‖. Clin. Neurophysiol, vol. 54, pp. 530–540, 1982.
[4]. Nicolaos B. Karayiannis, Amit Mukherjee, ―Detection of Pseudo sinusoidal Epileptic Seizure Segments in the Neonatal EEG by Cascading a Rule-Based Algorithm with a Neural Network‖ IEEE Transactions on Biomedical Engineering, vol. 53, no. 4, April 2006.
[5]. Vairavan Srinivasan, Chikkannan Eswaran, and Natarajan Sriraam, ―Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks‖, IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 3, May 2007.
[6]. Thasneem Fathima and M. Bedeeuzzaman , ―Wavelet Based Features for Epileptic Seizure Detection‖ , MES Journal of Technology and Management.
[7]. N. Sivasankari. And Dr. K. Thanushkodi , ,‖Automated Epileptic Seizure Detection in EEG Signals Using FastICA and Neural Network‖ Int. J. Advance. Soft Computer. Appl., Vol. 1, No. 2, November 2009.
[8]. N. McGrogen,‖Neural Networks detection of Epileptic Seizures in the Electroencephalogram‖,Probationary Research Transfer Report Oxford University infebruary 1999.
[9]. Dr. R. Shantha Selva Kumari, J.Prabin Jose ―Seizure Detection in EEG Using Time Frequency Analysis and SVM‖, IEEE 2011.