SEARCH WITHIN CONTENT
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 2, Issue 4, Pages 612-635, DOI: https://doi.org/10.21307/ijssis-2017-371
License : (CC BY-NC-ND 4.0)
Published Online: 03-November-2017
This paper aims to analyze the optimization of Epilepsy risk levels from EEG signals using Fuzzy based Elman-Chaotic Optimization. The EEG (Electroencephalogram) signals of twenty patients are collected from Sri Ramakrishna Hospitals at Coimbatore. The raw EEG signals are sampled and various parameters like energy, energy, variance, peaks, sharp and spike waves, duration, events and covariance. The fuzzy techniques are applied as a first level classifier to classify the risk levels of epilepsy by converting the EEG signal parameters in to code patterns by fuzzy systems. Elman-Chaotic optimization is identified as post classifiers on the classified data to obtain the optimized risk level that characterizes the patient’s epilepsy risk level. This classification provides a better way of treating the epileptic patients. This project aims to safeguard a patient’s life when critical situation occurs. Future scope is to design an embedded system which collects the raw EEG signals from the brain and directly gives the level of epilepsy. It will make the neural surgeons to give appropriate remedial measures.
 Leon D.Iasemidis etal., Adaptive Epileptic Seizure Prediction System, IEEE Transactions on Biomedical Engineering, May 2003,50(5): 616-627.
 K P Adlassnig, Fuzzy Set Theory in Medical diagnosis, IEEE Transactions on Systems Man Cybernetics, March 1986,16: 260-265.
 Alison A Dingle et al, A Multistage system to Detect epileptic form activity in the EEG,IEEE Transactions on Biomedical Engineering,1993, 40(12):1260-1268.
 Hauquo and Jean Gotman, A patient specific algorithm for detection onset in long-term EEG monitoring possible use as warning device, IEEE Transactions on Biomedical Engineering, February 1997, 44(2): 115-122.
 Arthur C Gayton, Text Book of Medical Physiology, Prism Books Pvt. Ltd., Bangalore, 9th Edition, 1996.
 J.Seunghan Park et al, TDAT Domain Analysis Tool for EEG Analysis, IEEE Transactions on Biomedical Engineering, August 1990,37(8): 803-811.
 Donna L Hudson, Fuzzy logic in Medical Expert Systems, IEEE EMB Magazine, November/December 1994,13(6): 693-698.
 C B Gupta and Vijay Gupta, An Introduction to Statistical Methods, 22nd Ed., Vikas Publishing House Lt., 2001.
 R.Harikumar and B.Sabarish Narayanan, Fuzzy Techniques for Classification of Epilepsy risk level from EEG Signals, Proceedings of IEEE Tencon – 2003, 14-17 October 2003,Bangalore, India, 209-213.
 Mark van Gils, Signal processing in prolonged EEG recordings during intensive care, IEEE EMB Magazine November/December 1997,16 (6): 56-63.
 R.Harikumar, Dr.(Mrs). R.Sukanesh, P.A. Bharthi, Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy Risk Levels from EEG signals,I.E . India Journal of Interdisciplinary panels, May 2005, 86(1):9-17.
 Celement.C etal, A Comparison of Algorithms for Detection of Spikes in the Electroencephalogram, IEEE Transaction on Bio Medical Engineering, April 2003, 50 (4): 521-26.
 Pamela McCauley-Bell and Adedeji B.Badiru, Fuzzy Modeling and Analytic Hierarchy Processing to Quantify Risk levels Associated with Occupational Injuries- Part I: The Development of Fuzzy- Linguistic Risk Levels, IEEE Transactions on Fuzzy Systems, 1996,4 ( 2): 124-31.
 Joel.J etal, Detection of seizure precursors from depth EEG using a sign periodogram transform, IEEE Transactions on Bio Medical Engineering, April 2004,51 (4):449-458.
 S.Vitabile etal.,Daily peak temperature forecasting with Elman neural networks, Proceedings of IEEE 2004, 2765-2769.
 Nurettin Acir etal., Automatic detection of epileptiform events in EEG by a three-stage procedure based artificial neural networks, IEEE transactions on Bio Medical Engineering January 2005,52(1):30-40.
 Drazen.S.etal., Estimation of difficult –to- Measure process variables using neural networks, Proceedings of IEEE MELECON 2004,May 12-15, 2004, Dubrovnik,Croatia, 387-390.
 Moreno.L.etal., Brain maturation estimation using neural classifier, IEEE Transaction of Bio Medical Engineering , April 1995,42(2):428-432.
 Tarassenko.L,Y.U.Khan,M.R.G.Holt,Identification of inter-ictal spikes in the EEG using neural network analysis, IEE Proceedings –Science Measurement Technology, November 1998,145(6):270-278.
 Hwang et al., Recognition of Unconstrained Handwritten Numerals by A Radial Basis Function Network Classifier, Pattern Recognition Letters, 1997,18:657-664.
 H.Demuth and M..Beale, Neural network tool box: User’s guide, Version 3.0 the math works, Inc., Natick, MA, 1998.
 G.Fung etal, Fault Detection In Inkjet Printers Using Neural Networks, Proceedings of IEEE SMC, 2002.
 Guoqiang Peter Zhang , Neural Networks for Classification: A Survey ,IEEE Transactions on Systems Man Cybernetics- Part C: Applications and Reviews, November 2000,30(4): 451-462.
 Jonathan lee etal., A Neural Network Approach to Cloud Classification, IEEE Transactions on Geosciences and Remote Sensing, September 1990,28 (5): 846-855.
 S.Haykin, Neural networks a Comprehensive Foundation, Prentice- Hall Inc. 2nd Ed. 1999.
 Mu-chun Su, Chien –Hsing Chou, A modified version of the k-means clustering algorithm with a distance based on cluster symmetry, IEEE Transactions on Pattern Analysis and Machine Intelligence June 2001, 23 (6): 674-680.
 Masaaki Tsujitani, Takashi Koshimizu, Neural Discriminant Analysis, IEEE Transactions on Neural Networks, November 2000,11 (6):1394-1401.
 Rangaraj M. Rangayyan, Bio- Medical Signal Analysis A Case Study Approach, IEEE Press-John Wiley &sons Inc New York 2002.
 Yuan-chu Cheng, Wei-Min Qi,WeiYou Cai, Dynamic Properties of Elman And Modified Elman Neural Network,Proceedings of IEEE, First International Conference on Machine Learning And Cybernatics, Beijing, 2002,637-640.
 K.Sriramamurty and B.Yegnannarayana, Combining Evidence from Residual Phase and MFCC Features for Speaker Recognition, IEEE Signal Processing Letters, January 2006,13 (1): 52-55.
 M.E. Cohen, Donna.L Hudson, and Prakash.C.D, applying continuous Chaotic modeling to cardiac signal analysis, IEEE EMBS Magazine, September 1996 vol,15, (5),pp 97-102.
 Albano.A.M., Muench.J., Schwartz. C, Mees.A.I. and Rapp.P.E, (1988) ‘Singularvalue decomposition and the Grassberger-Procaccia algorithm’Physical Review A,pp.3017-3026.
 Broomhead. D.S. and King .G.P, (1986) ‘Extracting qualitative dynamics from experimental data’ Physical Review D, pp.217-236.
 Farmer. J.D., Ott.E., Yorke. J.A. (1983) , ‘The dimension of chaotic attractors’ Physical Review D, pp.153-180
 Ivan Dvorak and Jarorair Siska, (1986), ‘On some problems encountered in calculating the correlation dimension of EEG’, International centre for Theoretical physics. International atomic energy agency, United Nations educational, scientific and cultural organization.
 M.E. Cohen, Donna.L Hudson, Inclusion of ECG and EEG Analysis in Neural Network Models, Proceedings of International Conference of the 23rd Annual IEEE EMBS October 25-28, 2001, Istanbul Turkey, pp 1621-1624.
 N.C. Bhavaraju, Mark.G.Frei and Ivan Osorio,Analog seizure Detection and Performance Evaluation, IEEE Transactions on BME Vol 53, (2), February 2006, pp 238-245.