Desired EEG Signals For Detecting Brain Tumor Using LMS Algorithm And Feedforward Network
 |
International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2012 by IJETT Journal | ||
| Volume-3 Issue-6 |
||
| Year of Publication : 2012 | ||
| Authors : Indu Sekhar Samant , Guru Kalyan Kanungo , Santosh Kumar Mishra |
Citation
Indu Sekhar Samant , Guru Kalyan Kanungo , Santosh Kumar Mishra. "Desired EEG Signals For Detecting Brain Tumor Using LMS Algorithm And Feedforward Network". International Journal of Engineering Trends and Technology (IJETT). V3(6):718-723 Nov-Dec 2012. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
In Brain tumor diagno stic EEG is the most relevant in assesing how basic functionality is affected by the lesion.EEG continues to be an attractive tool in clinical practice due to its non invasiveness and real time depication of brain function. But the EEG signa l contains the useful information along with redundant or noise information. In this Paper Least Mean Square algorithm is used to remove the artifact in the EEG signal. , generic features present in the EEG signal are extracted using spectral estimation . Specifically , spectral analysis is achieved by using Fast Fourier Transform that extracts the signal features buried in a wide band of noise . The desired signal is undergone as training and testing of FLANN to effectively classify the EEG signal with Bra in tumor
References
[1]. Habl , M.and Bauer , Ch . and Ziegaus , Ch., Lang , Elmarand Schulmeyer , F.“ Can I CA help identify brain tumorrelated EEG signals ? ” Proceedings / ICA 2000 , Second International Work - shopon Independent Component Analysis and Blind Signal Separation : Helsinki , Finland, June 19 - 22 , 2000 . Unspecified, pp . 609 - 614 . ISBN 951 - 22 - 5017 - 9
[2]. Fadi N. Karameh, Munther A.Dahleh “Automated Classification of EEG Signal in Brain tumour diagnostics” IEEE Proceeding of the American Control Conference, Chiago, llinois. June 2000.
[3]. Simon Haykin,”Adaptive Fi lter Theory’’,4 th Edition , Prentice Hall I nformation and System Sciences Series.
[4]. S. Sanei and J. A. Chambers, “ EEG Signal Processing ”, John Wiley & Sons, New York, NY, USA, 2007.
[5]. M. Murugesan and Dr. (Mrs.).R. Sukanesh “Towards Detection of Brain Tumor i n Electroencephalogram Signals Using Support Vector Machines” International Journal of Computer Theory and Engineering, Vol. 1, No. 5, December, 2009
[6] .J. A. Freeman and D. M. Skapura, "Neural networks: algorithms applications and programming techniqu es", Addison Wesley Longman,pp. 89 - 105, 1991.
[7] E. Haselsteiner, and G. Pfurtscheller, "Using time - dependent neural networks for EEG classification," IEEE Transactions on Rehabilitation Engineering, Vol. 8, pp 457 – 463, 2000
[8] A. Subasia, and E. Er celebi, "Classification of EEG signals using neural network and logistic regression," Computer Methods and Programs in Biomedicine, Vol. 78, pp. 87 - 99, 2005
[9] K Vijayalakshmi and Appaji M Abhishek “ Spike Detection in Epileptic Patients EEG Data using Template Matching Technique ” International Journal of Computer Applications (0975 – 8887 ) Vol 2 , No . 6 , June 2010
[10] Hojjat Adeli, Samanwoy Ghosh and Dastidat “Autoamted EEG Based Diagnosis of Neurological Disorders”, CRC Press; 1 edi - tion, 2 010
[11] Smith,J. “Automatic and Detection of EEG Spikes” , IEEE trans - actions on Bio medical Engineering ,Vol. BMF - 21:1 - 7,Jan,1974 .
[12] A. L. Betker, T. Sztur m, Z. Moussavi, "Application of Feedforward Backpropagation Neural Network to Center of Mass Es timation for Use in a Clinical Environment", Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,Vol. 3, pp. 2714 - 2717, 17 - 21 Sept. 2003.
[13] E. Haselsteiner , and G. Pfurts cheller , "Using time - dependent neural network s f o r EEG c la ss i f i ca t i o n," I EEE Tr a n s a ct io n s o n R e h a b i li t a t io n En g i n eeri n g , Vo l . 8 , p p 4 5 7 – 4 6 3 , 20 0 0
[14] S u b a si a , a n d E. E r c e l e b i , " Cl a ss i f i c a t i o n o f E EG s i g n a l s us i ng ne ur a l n e t w o r k a nd l o g i s t i c r e gr e ss io n," C o m p ut er M e t h o d s a n d Pr og r a ms i n B io m e d i c i n e, Vo l . 7 8 , p p . 8 7 - 9 9 , 20 0 5
[15] N . F . G u l e ra , E . D. Ub e y l i b, I . G u l e r , " R ec urr ent ne ura l net w o rk s e mp lo y i ng L y a pu n o v E x p o ne n ts f o r EEG s i g n a l s c l a ss i f i c a t i o n " E x p ert S ys t e m s wi t h App li c a t io n s, V o l . 2 9 , pp . 5 0 6 – 5 1 4 , 20 0 5
[16] G. Fil li g o i , M . P a d a l i n o , S. P i o l i “ A M a t l a b s o f tw a r e f o r d e t e c - t i o n & c o u nt i ng o f e pi l e p t i c s e i z u r es i n 7 2 h o ur s H o l te r - EE G ” Mu l t i d i s ci p l i n a r y J o u r n a l s i n S c i e nc e a n d Te c h n o l o g y , J o u r n a l o f S e - l e ct ed A r e a s i n B i o e n gi n ee r i n g (JSA B ) , J a n u a r y E d i t i o n , 2 01 1
Keywords
Brain Tumor ; CT ; EEG ; FLANN ; LMS .