Abstract

In the last couple of years, the EEG signal analysis was focused on epilepsy seizure detection. Epilepsy is a common chronic neurological disorder; they are result of transient and unexpected electrical disturbance of the brain. Epilepsy seizures also a symptom of brain tumor existence, 30% patients with brain tumor are affected with epilepsy seizure. This paper proposes a two level brain tumor epilepsy seizure identification method that combines bio-medical engineering techniques and clinical diagnosis data. First level classify the given EEG signal in to normal and epilepsy seizure, based on the first level input second level identifies the epilepsy seizure signal is from brain tumor or other neural disorders. Proposed method uses multi wavelet transform for feature extraction, in which EEG signal is decompose in to sub-bands. Irregularities present in the EEG signal are measured by using the approximate entropy. Feed forward neural network is used to classify input EEG signal as normal and brain tumor epilepsy signal. Obtained results are promising with first level epilepsy seizure identification accuracy of 93%.

References

• J. D. Bronzino, "Biomedical Engineering Handbook", New York: CRC Press LLC, Vol. I, 2nd edition, 2000.
• American Brain Tumor Association "Brain Tumor Primer – A Comprehensive Introduction to Brain Tumor", 9th edition, Available online. Last accessed September 24, 2012.
• Atlanta, Ga: "American Cancer Society: Cancer Facts and Figures 2012", American Cancer Society 2012. Available online. Last accessed September 24, 2012.
• Edward B Bromfield and Selim R Benbadis "EEG in Brain Tumors", Medscape Reference. Available online. Last accessed September 25, 2012.
• Selim R Benbadis "Encephalopathic EEG Patterns", Medscape Reference. Available online. Last accessed September 26, 2012.
• http://www. fil. ion. ucl. ac. uk/EEGvolunteerguide. pdf
• http://www. brain-surgery. com/primer. html
• Sharanreddy and Dr. P. K. Kulkarni "Review of Significant Research on EEG based Automated Detection of Epilepsy Seizures & Brain Tumor", International Journal of Scientific & Engineering Research, Volume 2, Issue 8, Aug-2011, ISSN 2229-5518.
• Sivasankari N and K. Thanushkodi, "Automated Epileptic Seizure Detection in EEG Signals Using Fast ICA and Neural Network", Int. J. Advance. Soft Comput. Appl. , Vol. 1, No. 2, pp. 91-104, November 2009.
• Ling Guo, Daniel Rivero and Alejandro Pazos, "Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks", Journal of Neuroscience Methods, Vol. 193, pp. 156-163, 2010.
• Kifah Tout, Nisrine Sinno and Mohamad Mikati, "Prediction of the Epileptic Events 'Epileptic Seizures' by Neural Networks and Expert Systems", International Journal of Biological and Medical Sciences, Vol. 5, No. 1, pp. 38-45, 2010.
• Laxman Tawade and Hemant Warpe, "Detection of Epilepsy Disorder Using Discrete Wavelet Transforms Using MATLABs", International Journal of Advanced Science and Technology, Vol. 28, pp. 17-24, March 2011.
• The testing dataset is referred from the link: http://physionet. fri. uni-lj. si/pn6/chbmit/.
• Ali Shoeb and john Guttag "Application of Machine Learning To Epileptic Seizure Detection" Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel. Omnipress 2010, ISBN 978-1-60558-907-7.
• M. Murugesan and Dr. (Mrs. ). R. Sukanesh "Towards Detection of Brain Tumor in Electroencephalogram Signals Using Support Vector Machines" International Journal of Computer Theory and Engineering, Vol. 1, No. 5, December, 2009.
• Sharanreddy and Dr. P. K. Kulkarni "Literature Survey on EEG based Automatic Diagnosis of Epilepsy seizures & Brain Tumor using WT and ANN" International Conference on Biomedical Engineering (ICBME 2011), Dec 10-12, 2011, Manipal, India.
• Sharanreddy and Dr. P. K. Kulkarni "Necessity for Automated Analysis of EEG Signal for Detection of Multiple Neurological Disorders" International Conference on Evolutionary Trends in Information Technology (ICETIT 2012), Sep 15-17, 2012, VTU Belgaum, India.
• Sharanreddy and Dr. P. K. Kulkarni "An Improved Approximate Entropy Based Epilepsy Seizure Detection Using Multi-Wavelet and Artificial Neural Networks" International Journal of Biomedical Engineering and Technology, Accepted – 22 Jan 2013, InderScience Publishers, UK, ISSN online: 1752-6426, ISSN print: 1752-6418.
• Sharanreddy and Dr. P. K. Kulkarni "Multi-Wavelet Transform Based Epilepsy Seizure Detection" Proceedings – 2012 IEEE EMBS Conference on Bio Engineering & Sciences (IECBES 2012) Dec. 17-19, 2012, Langkawi, Malaysia. ISBN - 978-1-4673-1666-8.
• Sharanreddy and Dr. P. K. Kulkarni "Detection of primary brain tumor present in eeg signal using wavelet transform and neural network" International Journal of Biological and Medical Research, 2013; 4(1): ISSN 2855-2859.
• Seenwasen Chetty, Ganesh K. Venayagamoorthy "An investigation into the Detection of brain tumours using electroencephalography (EEG) signals with Artificial neural networks" Computational Intelligence Group, Department of Electronic Engineering M L Sultan Technikon.
• Habl, M. and Bauer, Ch. and Ziegaus, Ch. , Lang, Elmar and Schulmeyer, F. "Can ICA help identify brain tumor related EEG signals?" Proceedings / ICA 2000, Second International Workshop on Independent Component Analysis and Blind Signal Separation: Helsinki, Finland, June 19 - 22, 2000. Unspecified, pp. 609-614. ISBN 951-22-5017-9.
• Small, Joyce Graham Bagchi, Basu K. Kooi, Kenneth A. "Electro-clinical profile of 117 deep cerebral tumors" Elsevier Inc Electroencephalography and Clinical Neurophysiology, Vol 13, Issue 2 , Pages 193-207, April 1961
• Forrest Sheng Bao, Donald Yu-Chun Lie, and Yuanlin Zhang, "A New Approach to Automated Epileptic Diagnosis Using EEG and Probabilistic Neural Network", in Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence, Vol. 02, pp. 482-486, 2008.
• Steven Walczak and William J. Nowack, "An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs", Journal of Medical Systems, Vol. 25, No. 1, pp. 9-20, February 2001
• Maan M. Shaker, "EEG Waves Classifier using Wavelet Transform and Fourier Transform", International Journal of Biomedical Sciences, Vol 1, No 2, 2006, ISSN 1306-1216.
• Nick Yeung, Rafal Bogacz, Clay B. Holroyd and Jonathan D. Cohen "Detection of synchronized oscillations in the electroencephalogram: An evaluation of methods" Psychophysiology, 41 (2004), 822–832. Blackwell Publishing Inc. Printed in the USA.
• Samhita P, Venkataraman V, Radhakrishnan, Kurupath Radhakrishnan, Ravi M, and Sankara P. "Electro-clinical characteristics and postoperative outcome of medically refractory tumoral temporal lobe epilepsy" Vol 53, Issue 1, Neurology of India, March 2005.
• Young Zoon Kim, Eun Hee Lee and Kyoung Soo Lee "Clinical Analysis for Brain Tumor-Related Epilepsy during Chemotherapy for Systemic Cancer with Single Brain Metastasis" Cancer Res Treat. 2011; 43(3):160-169.
• M. Gelabert-González?, J. M. Santín Amo, A. Arcos Algaba, R. Serramito García "Intracranial gangliogliomas. A review of a series of 20 patients", Sociedad Espa˜nola de Neurología. Published by Elsevier España, 2010.