Abstract
Epileptic Seizures occur as a result of certain electrical action in the brain. This makes the patient behave abnormally for a limited amount of time. The electrical activity can be measured with the help electrodes attached to different areas of the scalp to capture the EEG signals. Usually, the signals from the aforementioned device are interpreted by the specialists who specialize in this very thing but their detection is susceptible to errors which prove fatal in some cases. This paper provides an automated system which will detect epileptic seizure without involving an expert opinion. The proposed system goes through a four step process i.e. pre-processing, where the data is organized to suit the system processing and noise is removed. Then temporal and spectral feature extraction is performed. The system then applies the feature selection procedure to extract best set of features which are finally passed to the next phase for classification of EEG signals as normal or abnormal. The suggested system is established on a publicly open dataset and provides an average accuracy of 86.93 %.
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References
Alexandros, T.T.: Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703–710 (2009)
Exarchos, T.P., Tzallas, A.T., Fotiadis, D.I., Konitsiotis, S., Giannopoulos, S.: EEG transient event detection and classification using association rules. IEEE Trans. Inf. Technol. Biomed. 10(3), 451–457 (2006)
Gotman, J.: Automatic detection of seizures and spikes. J. Clin. Neurophysiol. 16(2), 130–140 (1999)
Rabbi, A.F., Fazel-Rezai, R.: A fuzzy logic system for seizure onset detection in intracranial EEG. Comput. Intell. Neurosc. 2012, Article ID 705140, 12 pp (2012). doi:10.1155/2012/705140
Sharanreddy, M.A., Kulkarni, P.K.: EEG Signal classification for epilepsy seizure detection using improved approximate entropy. Int. J. Public Health Sci. (IJPHS) 2(1), 23–32 (2013). ISSN: 2252–8806
Khan, Y.U., Farooq, O., Sharma, P.: Automatic detection of seizure onset in pediatric EEG. Int. J. Embed. Syst. Appl. (IJESA) 2(3), 81–89 16 pp (2012)
Shoeb, A.H., Guttag, J.V.: Application of machine learning to epileptic seizure detection. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 975–982 (2010)
Kemp, B., Varrib, A., Rosac, A.C., Nielsend, K.D., Gade, J.: A simple format for exchange of digitized polygraphic recordings, Electroencephalogr. Clin. Neurophysiol. 82, 391–393 (1992)
Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropiesfor detection of epilepsy in EEG. Comput. Methods Prog. Biomed. 80, 187–194 (2005)
Mitchell, Douglas W.: More on spreads and non-arithmetic means. The Mathematical Gazette 88, 142–144 (2004)
Upton, G., Cook, I.: Understanding Statistics. Oxford University Press, Oxford, p. 55 (1996)
Kader, G.: Means and MADS. Math. Teach. Middle Sch. 4(6), 398–403 (March 1999). Retrieved 20 Feb 2013
Dean, S., Illowsky, B.: Descriptive Statistics: Skewness and the Mean, Median, and Mode
Ballanda, Kevin P., MacGillivray, H.L.: Kurtosis: A Critical Review. Am. Stat. (American Statistical Association) 42(2), 111–119 (1988)
Adeli, H., Zhou, Z., Dadmehr, N.: Analysis of EEG records inan epileptic patient using wavelet transform. J. Neurosci. Methods 123, 69–87 (2003)
Marple, S.L.: Digital Spectral Analysis. Prentice-Hall, Englewood Cliffs, pp. 373–378 (1987)
Lowry, R.: Concepts and Applications of Inferential Statistics. Retrieved 24 March 2011
Bauer, D.F.: Constructing con dence sets using rank statistics. J. Am. Stat. Assoc. 67, 687–690 (1972)
Akram, M.U., Tariq, A., Anjum, M.A., Javed, Y.: Automated detection of exudates in colored retinalimages for diagnosis of diabetic retinopathy. Appl. Opt. 51(20)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 1st edn. Academic, New York (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
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Gill, A.F., Fatima, S.A., Usman Akram, M., Khawaja, S.G., Awan, S.E. (2015). Analysis of EEG Signals for Detection of Epileptic Seizure Using Hybrid Feature Set. In: Sulaiman, H., Othman, M., Abd. Aziz, M., Abd Malek, M. (eds) Theory and Applications of Applied Electromagnetics. Lecture Notes in Electrical Engineering, vol 344. Springer, Cham. https://doi.org/10.1007/978-3-319-17269-9_6
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DOI: https://doi.org/10.1007/978-3-319-17269-9_6
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