EEG Based Epileptic Seizure Detection

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-33 Number-4
Year of Publication : 2016
Authors : Siddharth Shah, Vishakha Sasane, Simantini Vardam, Vishal Bharate
DOI :  10.14445/22315381/IJETT-V33P237

Citation 

Siddharth Shah, Vishakha Sasane, Simantini Vardam, Vishal Bharate"EEG Based Epileptic Seizure Detection", International Journal of Engineering Trends and Technology (IJETT), V33(4),191-195 March 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Epilepsy is common neurological disorders that greatly impair patient daily lives[1]. Traditional epileptic diagnosis relies on lengthy EEG recording that requires the presence of seizure (ictal) activities.EEG has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. The wavelet transform with statistic values to extract features and tested the performance of system by Support Vector Machines are the best cascading technique for EEG signal analysis. The wavelet transform can be use for feature extraction and obtain statistical parameters from the decomposed wavelet coefficients and A Support Vector Machine is used for the classification.

 References

[1] D.Satheesh Kumar , Dr. K.P.Yadav. "Medullablastoma Neoplasm in MR Images Dissection on FCM Method". International Journal of Engineering Trends and Technology (IJETT). V4(1):84-92 Jan 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
[2] Ms. V. Kavitha , Mr. S. Rajesh Kumar Reddy, Article: Segmentation of Gray Matter, White Matter and Brain Tumour from Brain MR Images, International Journal of Engineering Trends and Technology(IJETT), 7(2),79-85, published by seventh sense research group.
[3] Vivek Khirasaria, Bhadreshsinh Gohil"A Survey on Detection and Blocking of Image Spammers", International Journal of Engineering Trends and Technology (IJETT), V30(1),29-32 December 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
[4] Alekhyasuma, P.Rajasekhar"A Hybrid Supervised and Unsupervised Learning Approach for Node Classification", International Journal of Engineering Trends and Technology (IJETT), V32(4),171-174 February 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
[5] R. Panda, P. S. Khobragade, P. D. Jambhule, S. N. Jengthe, P. R. Pal and T. K. Gandhi, “Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction,” IEEE Transactions On Systems in Medicine and Biology (ICSMB),pp. 405-408, 2010.
[6] Chia-Ping Shen, Chih-Min Chan, Feng-Sheng Lin, Ming- Jang Chiu, Jeng-Wei Lin, Chung-Ping Chen, Feipei Lai and Jui-Hung Kao, “Epileptic Seizure Detection for Multichannel EEG Signals with Support Vector Machines,” IEEE International Conference on Bioinformatics and Bioengineering, pp. 39-43, 2011.
[7] Yusuf U Khan, Omar Farooq and Priyanka Sharma, “Automatic Detection Of Seizure Onset In Pediatric Eeg,” International Journal of Embedded Systems and Applications (IJESA), Vol.2, No.3, September 2012.
[8] L.M. Patnaika, Ohil K. Manyamb, “Epileptic EEG detection using neural networks and post-classification,” computer methods and programs in biomedicine., pp. 100109, 2008.
[9] Kavya Devarajan, S. Bagyaraj, Vinitha Balasampath, Jyostna. E. and Jayasri. K., “EEGBased Epilepsy Detection and Prediction,” IACSIT International Journal of Engineering and Technology.,Vol. 6, No. 3, June 2014.
[10] Joel J. Niederhauser, Rosana Esteller, Javier Echauz, George Vachtsevanos and Brian Litt, “Detection of Seizure Precursors From Depth-EEG Using a Sign Periodogram Transform,”IEEE Transactions on Biomedical Engineering.,Vol. 51, No. 4, April 2003.

Keywords
EEG, support vector machine , entropy , seizure, electroencephalogram , brain, epilepsy.