Alternative Form of Ordinary Differential Equation of Electroencephalography Signals During an Epileptic Seizure

Ameen Omar Barja


One of the most important fields in clinical neurophysiology is an electroencephalogram (EEG). It is a test used to detect problems related to the brain electrical activity, and it can track and records patterns of brain waves. EEG continues to play an essential role in diagnosis and management of patients with epileptic seizure disorders. Nevertheless, the outcome of EEG as a tool for evaluating epileptic seizure is often interpreted as a noise rather than an ordered pattern. The mathematical modelling of EEG signals provides valuable data to neurologists, and is heavily utilized in the diagnosis and treatment of epilepsy. EEG signals during the seizure can be modeled as ordinary differential equation (ODE). In this study we will present an alternative form of ODE of EEG signals through the seizure.


EEG signals; ODE; Initial time

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Fisher, R.S., et al., ILAE official report: a practical clinical definition of epilepsy. Epilepsia, 2014. 55(4): p. 475-482.

Duncan, J.S. and H.J. Cross, Epilepsy and the overlap with autonomic disorders. Autonomic Failure: A Textbook of Clinical Disorders of the Autonomic Nervous System, 2013: p. 435.

Nadkarni, S., J. LaJoie, and O. Devinsky, Current treatments of epilepsy. Neurology, 2005. 64(12 suppl 3): p. S2-S11.

Niedermeyer, E., D.L. Schomer, and F.H.L. da Silva, Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. 2011: Wolters Kluwer Health/Lippincott Williams & Wilkins.

Quiroga, R.Q., Quantitative analysis of EEG signals: time-frequency methods and chaos theory. Institute of Physiology-Medical University Lubeck and Institute of Signal Processing-Medical University Lubeck, 1998.

Babloyantz, A. and A. Destexhe, Low-Dimensional Chaos in an Instance of Epilepsy. Proceedings of the National Academy of Sciences, 1986. 83(10): p. 3513-3517.

Stam, C.J., Nonlinear Dynamical Analysis of EEG and MEG: Review of an Emerging Field. Clinical Neurophysiology, 2005. 116(10): p. 2266-2301.

Iasemidis, L.D., et al., Phase space Topography and the Lyapunov Exponent of Electrocorticograms in Partial Seizures. Brain Topography, 1990. 2(3): p. 187-201.

Frank, G., et al., Chaotic Time Series Analyses of Epileptic Seizures. Physica D: Nonlinear Phenomena, 1990. 46(3): p. 427-438.

Ahmad, T., et al., Development of Detection Model for Neuromagnetic Fields. Proceedings of the BIOMED, 2000: p. 119-121.

Ahmad, T., et al., Fuzzy Topographic Topological Mapping for Localisation Simulated Multiple Current Sources of MEG. Journal of Interdisciplinary Mathematics, 2008. 11: p. 381–393.

Ahmad, T., et al. Selection of A subset of EEG Channels of Epileptic Patient During Seizure Using PCA. in Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation. 2008. World Scientific and Engineering Academy and Society (WSEAS).

Ahmad, T., et al. Dynamical System of an Epileptic Seizure. in International Conference on New Techniques in Pharmaceutical and Biomedical Research. 2005. Kuala Lumpur, Malaysia: IEEE.

Kasanovich, B.R., Signal and System Analysis in Fuzzy Infonnation Space. 1995, University of Pittsburgh: United States of America.

Teschl, G., Ordinary Differential Equations and Dynamical Systems. Vol. 140. 2012: American Mathematical Soc.

Bhatia, N.P. and G.P. Szegö, Dynamical Systems: Stability Theory and Applications. 2006: Springer Berlin Heidelberg.

Barja, A.O.A., Epileptic Seizure as a System of Ordinary Differential Equation, in Mathematics. 2012, Universiti Teknologi Malaysia: UTM.



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