Prediction of the Epileptic Events 'Epileptic Seizures' by Neural Networks and Expert Systems
Many studies have focused on the nonlinear analysis
of electroencephalography (EEG) mainly for the characterization of
epileptic brain states. It is assumed that at least two states of the
epileptic brain are possible: the interictal state characterized by a
normal apparently random, steady-state EEG ongoing activity; and
the ictal state that is characterized by paroxysmal occurrence of
synchronous oscillations and is generally called in neurology, a
seizure.
The spatial and temporal dynamics of the epileptogenic process is
still not clear completely especially the most challenging aspects of
epileptology which is the anticipation of the seizure. Despite all the
efforts we still don-t know how and when and why the seizure
occurs. However actual studies bring strong evidence that the
interictal-ictal state transition is not an abrupt phenomena. Findings
also indicate that it is possible to detect a preseizure phase.
Our approach is to use the neural network tool to detect interictal
states and to predict from those states the upcoming seizure ( ictal
state). Analysis of the EEG signal based on neural networks is used
for the classification of EEG as either seizure or non-seizure. By
applying prediction methods it will be possible to predict the
upcoming seizure from non-seizure EEG.
We will study the patients admitted to the epilepsy monitoring
unit for the purpose of recording their seizures. Preictal, ictal, and
post ictal EEG recordings are available on such patients for analysis
The system will be induced by taking a body of samples then
validate it using another. Distinct from the two first ones a third body
of samples is taken to test the network for the achievement of
optimum prediction. Several methods will be tried 'Backpropagation
ANN' and 'RBF'.
[1] Josefina Gutierrez, Rogelio Alcantara, and Veronica Medina, "Analysis
and Localization of Epileptic Events Using Wavelet Packets" Medical
Engineering & Physics, Oct. 23, 2001.
[2] M. A. Cody, "The fast wavelet transform", Dr Dobb-s Journal, pp. 16-
28., April 1992.
[3] A. Bruce, D. Donoho, and H. Y. Gao, "Wavelet Analysis". IEEE
Spectrum, pp. 26-35, October 1996.
[4] Josefina Gutierrez, Rogelio Alcantara , "Spikes characterization on EEG
signal by wavelet coefficients". Fifth Conference of the European
Society for Engineering and Medicine Abstracts, 1999, pp.159-160.
[5] R. Cerf, H. el Ouasdad, and M. el Amri, "EEG-detected episodes of lowdimensional
self-organized cortical activity and the concept of a brain
attractor", In Uhl C(ed) Analysis of neurophysiological brain
functioning. Springer, Berlin Heidelberg New York, pp126-144 ,1999.
[6] H. Haken, Principles of brain functioning: a synergetic approach to
brain activity, behavior and cognition. Springer Verlag, Berlin
Heidelberg New York, 1996.
[7] A. Fuchs, J.A.S. Kelso, H. Haken "Phase transitions in the human brain
spatial mode dynamics". Int J Bifurec Chaos 2, pp. 917-939.
[8] Roger Cerf, El Hassan El Ouasdad, Philippe Kahane, "Criticality and
synchrony of fluctuations in rhythmical brain activity: pretransitional
effects in epileptic patients", 2004.
[9] Generoso Gascon and Mohamed Mikati, "Seizures and Epilepsy",
Department of Clinical Neurosciences, Brown University School of
Medicine and Department of Pediatrics, American University of Beirut,
School of Medicine.
[10] Walter Van Emde Boas and Jaime Parra, "Long-Term Noninvasive
Video Electroencephalographic Monitoring in Temporal Lobe
Epilepsy", Department of Electroencephalography and Epilepsy
Monitoring Unit, Epilepsy Clinic Meer & Bosch, Dutch Epilepsy Clinics
Foundation, the Netherlands,.
[11] Leon Iasemidis, Deng-shan shiau, Chris Sackellares, Panos Pardalos,
and Awadhesh Prasad, "Dynamical Resetting of the Human Brain at
Epileptic Seizures", IEEE Transactions on Biomedical Engineering, vol.
51, no. 3 march 2004.
[12] Roger Cerf, El Hassan El Ouasdad, and Philippe kahane, "Criticality and
Synchrony of Fluctuations in Rhythmical Brain Activity: Pretransitional
Effects in Epileptic Patients", Biological Cybernetics, March 2004.
[13] L. Tarassenko, "Neural Network Detection of Epileptic Seizures in the
Electroencephalogram", Oxford University, Department of Engineering
Science,. February, 1999.
[14] José Principe, Neil Euliano, and W.Curt Lefebvre. Neural and Adaptive
Systems, John Wiley & Sons, Inc, 2000.
[15] Russell Eberhart, Roy Dobbins. "Neural Network PC Tools", Academic
Press, Inc, 1990.
[16] M. .Mikati, Système d-enregistrement digital du signal EEG, Internal
Report, American University Hospital, Beirut, July, 2004.
[17] Ahmad Fadi, "Analyse Des Evénements Epileptique a l-aide Des
Transformées et Paquets d-Ondelettes", M.S. Thesis, Dept. Comp.
Science, Lebanese University,. Dec. 2004.
[18] Jerome T. Connor, R. Douglas Martin, and L. E. Atlas, "Recurrent
Neural Networks and Robust Time Series Prediction", IEEE
transactions on neural networks, vol. 5, no. 2, March 1994.
[19] R. Mikolajczak, "Comparative study of logistic map series prediction
using feed-forward, partially recurrent and general regression networks",
In Proceedings of the 9th International Conference on Neural
Information Processing ICONIP, Volume 5, Issue 18-22, Nov. 2002 ,
pp. 2364 - 2368.
[1] Josefina Gutierrez, Rogelio Alcantara, and Veronica Medina, "Analysis
and Localization of Epileptic Events Using Wavelet Packets" Medical
Engineering & Physics, Oct. 23, 2001.
[2] M. A. Cody, "The fast wavelet transform", Dr Dobb-s Journal, pp. 16-
28., April 1992.
[3] A. Bruce, D. Donoho, and H. Y. Gao, "Wavelet Analysis". IEEE
Spectrum, pp. 26-35, October 1996.
[4] Josefina Gutierrez, Rogelio Alcantara , "Spikes characterization on EEG
signal by wavelet coefficients". Fifth Conference of the European
Society for Engineering and Medicine Abstracts, 1999, pp.159-160.
[5] R. Cerf, H. el Ouasdad, and M. el Amri, "EEG-detected episodes of lowdimensional
self-organized cortical activity and the concept of a brain
attractor", In Uhl C(ed) Analysis of neurophysiological brain
functioning. Springer, Berlin Heidelberg New York, pp126-144 ,1999.
[6] H. Haken, Principles of brain functioning: a synergetic approach to
brain activity, behavior and cognition. Springer Verlag, Berlin
Heidelberg New York, 1996.
[7] A. Fuchs, J.A.S. Kelso, H. Haken "Phase transitions in the human brain
spatial mode dynamics". Int J Bifurec Chaos 2, pp. 917-939.
[8] Roger Cerf, El Hassan El Ouasdad, Philippe Kahane, "Criticality and
synchrony of fluctuations in rhythmical brain activity: pretransitional
effects in epileptic patients", 2004.
[9] Generoso Gascon and Mohamed Mikati, "Seizures and Epilepsy",
Department of Clinical Neurosciences, Brown University School of
Medicine and Department of Pediatrics, American University of Beirut,
School of Medicine.
[10] Walter Van Emde Boas and Jaime Parra, "Long-Term Noninvasive
Video Electroencephalographic Monitoring in Temporal Lobe
Epilepsy", Department of Electroencephalography and Epilepsy
Monitoring Unit, Epilepsy Clinic Meer & Bosch, Dutch Epilepsy Clinics
Foundation, the Netherlands,.
[11] Leon Iasemidis, Deng-shan shiau, Chris Sackellares, Panos Pardalos,
and Awadhesh Prasad, "Dynamical Resetting of the Human Brain at
Epileptic Seizures", IEEE Transactions on Biomedical Engineering, vol.
51, no. 3 march 2004.
[12] Roger Cerf, El Hassan El Ouasdad, and Philippe kahane, "Criticality and
Synchrony of Fluctuations in Rhythmical Brain Activity: Pretransitional
Effects in Epileptic Patients", Biological Cybernetics, March 2004.
[13] L. Tarassenko, "Neural Network Detection of Epileptic Seizures in the
Electroencephalogram", Oxford University, Department of Engineering
Science,. February, 1999.
[14] José Principe, Neil Euliano, and W.Curt Lefebvre. Neural and Adaptive
Systems, John Wiley & Sons, Inc, 2000.
[15] Russell Eberhart, Roy Dobbins. "Neural Network PC Tools", Academic
Press, Inc, 1990.
[16] M. .Mikati, Système d-enregistrement digital du signal EEG, Internal
Report, American University Hospital, Beirut, July, 2004.
[17] Ahmad Fadi, "Analyse Des Evénements Epileptique a l-aide Des
Transformées et Paquets d-Ondelettes", M.S. Thesis, Dept. Comp.
Science, Lebanese University,. Dec. 2004.
[18] Jerome T. Connor, R. Douglas Martin, and L. E. Atlas, "Recurrent
Neural Networks and Robust Time Series Prediction", IEEE
transactions on neural networks, vol. 5, no. 2, March 1994.
[19] R. Mikolajczak, "Comparative study of logistic map series prediction
using feed-forward, partially recurrent and general regression networks",
In Proceedings of the 9th International Conference on Neural
Information Processing ICONIP, Volume 5, Issue 18-22, Nov. 2002 ,
pp. 2364 - 2368.
@article{"International Journal of Medical, Medicine and Health Sciences:55626", author = "Kifah Tout and Nisrine Sinno and Mohamad Mikati", title = "Prediction of the Epileptic Events 'Epileptic Seizures' by Neural Networks and Expert Systems", abstract = "Many studies have focused on the nonlinear analysis
of electroencephalography (EEG) mainly for the characterization of
epileptic brain states. It is assumed that at least two states of the
epileptic brain are possible: the interictal state characterized by a
normal apparently random, steady-state EEG ongoing activity; and
the ictal state that is characterized by paroxysmal occurrence of
synchronous oscillations and is generally called in neurology, a
seizure.
The spatial and temporal dynamics of the epileptogenic process is
still not clear completely especially the most challenging aspects of
epileptology which is the anticipation of the seizure. Despite all the
efforts we still don-t know how and when and why the seizure
occurs. However actual studies bring strong evidence that the
interictal-ictal state transition is not an abrupt phenomena. Findings
also indicate that it is possible to detect a preseizure phase.
Our approach is to use the neural network tool to detect interictal
states and to predict from those states the upcoming seizure ( ictal
state). Analysis of the EEG signal based on neural networks is used
for the classification of EEG as either seizure or non-seizure. By
applying prediction methods it will be possible to predict the
upcoming seizure from non-seizure EEG.
We will study the patients admitted to the epilepsy monitoring
unit for the purpose of recording their seizures. Preictal, ictal, and
post ictal EEG recordings are available on such patients for analysis
The system will be induced by taking a body of samples then
validate it using another. Distinct from the two first ones a third body
of samples is taken to test the network for the achievement of
optimum prediction. Several methods will be tried 'Backpropagation
ANN' and 'RBF'.", keywords = "Artificial neural network (ANN), automatic
prediction, epileptic seizures analysis, genetic algorithm.", volume = "2", number = "5", pages = "159-8", }