Abstract
The electroencephalogram (EEG) is most often used signal to detect epileptic seizures. For a successful epilepsy surgery, it is very important to localize epileptogenic area. In this paper, a new method is proposed to classify focal and non-focal EEG signals. EEG signal is decomposed by empirical mode decomposition (EMD). The average Renyi entropy and the average negentropy of IMFs for EEG signals have been computed as features. The class discrimination ability of these features are quantified using Kruskal—Wallis statistical test. These features are set to input in neural network classifier for classification of focal and non-focal EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of focal and non-focal EEG signals.
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References
Andrzejak, R.G., Schindler, K., and Rummel, C.: Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Physical Review E 86, 046206 (2012).
Pati, S., and Alexopoulos, A.V.: Pharmacoresistant epilepsy: From pathogenesis to current and emerging therapies. Cleveland Clinic Journal of Medicine 77, 457-467 (2010).
Seeck, M., Lazeyras, F., Michel, C.M., Blanke, O., Gericke, C.A., Ives, J., Delavelle, J., Golay, X., Haenggeli, C.A., Tribolet, N.D., and Landis, T.: Non-invasive epileptic focus localization using EEG-triggered functional MRI and electromagnetic tomography. Electroencephalography and Clinical Neurophysiology 106, 508-512 (1998).
Savic, I., Thorell, J.O., and Roland, P.: Flumazenil positron emission tomography visualizes frontal epileptogenic regions. Epilepsia 36, 1225-1232 (1995).
Newton, M.R., Berkovic, S.F., Austin, M.C., Rowe, C.C., McKay, W.J., and Bladin, P.F.: SPECT in the localisation of extratemporal and temporal seizure foci. Journal of Neurology, Neurosurgery & Psychiatry 59, 26-30 (1995).
Spanaki, M.V., Spencer, S.S., Corsi, M., MacMullan, J., Seibyl, J., and Zubal, I.G.: Determining surgical candidacy in temporal lobe epilepsy. Journal of nuclear medicine: official publication, Society of Nuclear Medicine 40, 730-736 (1999).
Henry,T.R., and Heertum, R.L.V.: Positron emission tomography and single photon emission computed tomography in epilepsy care. Seminars in Nuclear Medicine 33, 88-104 (2003).
Knowlton, R.C., Elgavish, R.A., Bartolucci, A., Ojha, B., Limdi, N., Blount, J., Burneo, J.G., Hoef, L.V., Paige, L., Faught, E., Kankirawatana, P., Riley, K., and Kuzniecky, R.: Functional imaging: II. prediction of epilepsy surgery outcome. Annals of Neurology 64, 35-41 (2008).
Zhu, G., Li, Y., Wen, P.P., Wang, S, and Min Xi,: Epileptogenic focus detection in intracranial EEG based on delay permutation entropy. International Symposium on Computational Models For Life Sciences 1559, 31-36 (2013)
Sharma, R., Pachori, R.B., and Gautam, S.: Empirical mode decomposition based classification of focal and non-focal EEG signals. International Conference on Medical Biometrics, 135-140 (2014)
Flandrin, P., Rilling, G., and Goncalves, P.: Empirical mode decomposition as a filter bank. IEEE Signal Processing Letters 11, 112-114 (2004)
Renyi, A.: On measures of entropy and information. Proceedings of the fourth Berkeley Symposium on Mathematics, Statistics and Probability 1, 547-561 (1961)
Leon, B.: Negentropy principle of information. Journal of Applied Physics 24, 1152-1163 (1953)
Yegnanarayana, B.: Artificial Neural Networks, Prentice-Hall of India, New Delhi, (1999)
MacKay, D.J.C.: Bayesian interpolation, Neural Computation 4, 415-447 (1991)
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Rai, K., Bajaj, V., Kumar, A. (2015). Features extraction for classification of focal and non-focal EEG signals. In: Kim, K. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46578-3_70
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DOI: https://doi.org/10.1007/978-3-662-46578-3_70
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