Abstract
The ability to predict epileptic seizures well prior to their clinical onset provides promise for new diagnostic applications and novel approaches to seizure control. Several groups of investigators have reported that it may be possible to predict seizures based on the quantitative analysis of EEG signal characteristics. The objective of this chapter is first to report an automated seizure warning algorithm, and second to compare its performance with other, theoretically sound, statistical algorithms. The proposed automated seizure prediction algorithm (ASPA) consists of an optimization method for the selection of critical cortical sites using measures from nonlinear dynamics, and a novel method for the detection of preictal transitions using adaptive transition thresholds according to the current state of dynamical interactions among brain sites. Continuous long-term (mean 210 hours per patient) intracranial EEG recordings obtained from ten patients with intractable epilepsy (total of 130 recorded seizures) were analyzed to test the proposed algorithm. For each patient, the prediction ROC (receiver operating characteristic) curve, generated from ASPA, was compared with the ones from periodic and random prediction schemes. The results showed that the performance of ASPA is significantly superior to each naïve prediction method used (p-value < 0.05). This suggests that the proposed nonlinear dynamical analysis of EEG contains relevant information to prospectively predict an impending seizure, and thus has potential to be useful in clinical applications.
This research is supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) via a Bioengineering Research Partnership grant for Brain Dynamics (8R01EB002089-03). The facilities used for this research were the Brain Dynamics Laboratories at the University of Florida, Gainesville, FL and at the Arizona State University, Tempe, AZ, USA.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
R. Andrzejak, F. Mormann, T. Kreuz, et al. Testing the null hypothesis of the nonexistence of a Preseizure state. Physical Review E, 67: 010901-1–4, 2003.
R. Aschenbrenner-Scheibe, T. Maiwald, M. Winterhaider, H.U. Voss, J. Timmer, and A. Schulze-Bonhage. How well can epileptic seizures be predicted? An evaluation of a nonlinear method. Brain, 126: 1–11, 2003.
C.E. Elger and K. Lehnertz. Seizure prediction by non-linear time series analysis of brain electrical activity. European Journal of Neuro science, 10: 786–789, 1998.
L.D. Iasemidis, P.M. Pardalos, D.-S. Shiau, W. Chaovalitwongse, M. Narayanan, S. Kumar, P.R. Carney, and J.C. Sackellares. Prediction of Human Epileptic Seizures based on Optimization and Phase Changes of Brain Electrical Activity. Optimization Methods and Software, 18(1): 81–104, 2003.
L.D. Iasemidis, J.C. Principe, J.M. Czaplewski, R.L. Gilmore, S.N. Roper, and J.C. Sackellares. Spatiotemporal transition to epileptic seizures: A nonlinear dynamical analysis of scalp and intracranial EEG recordings. In F.H. Lopes de Silva, J.C. Principe, L.B. Almeida, editors, Spatiotemporal Models in Biological and Artificial Systems, pages 81–88. IOS Press, Amsterdam, 1997.
L.D. Iasemidis, J.C. Sackellares, H.P. Zaveri, W.J. Williams. Phase space topography of the electrocorticogram and the Lyapunov exponent in partial seizures. Brain Topography, 2: 187–201, 1990.
L.D. Iasemidis and J.C. Sackellares. The temporal evolution of the largest Lyapunov exponent on the human epileptic cortex. In D.W. Duck and W.S. Pritchard, editors. Measuring Chaos in the Human Brain, pages 49–82. World Scientific, Singapore, 1991.
L.D. Iasemidis, D.-S. Shiau, W. Chaovalitwongse, et al. Adaptive epileptic seizure prediction system. IEEE Transactions in Biomedical Engineering, 50(5): 616–627, 2003.
L.D. Iasemidis, D.-S. Shiau, J.C. Sackellares, P.M. Pardalos, and A. Prasad. Dynamical resetting of the human brain at epileptic seizures: application of nonlinear dynamics and global optimization techniques. IEEE Transactions on Biomedical Engineering, 51(3): 493–506, 2004.
L.D. Iasemidis. On the Dynamics of Human Brain in Temporal Lobe Epilepsy. PhD thesis, University of Michigan, Ann Arbor, 1991.
L.D. Iasemidis, P.M. Pardalos, J.C. Sackellares, and D.-S. Shiau. Quadratic binary programming and dynamical system approach to determine the predictability of epileptic seizures. Journal of Combinatorial Optimization, 5: 9–26, 2001.
L. Iasemidis, D.-S. Shiau, P.M. Pardalos, and J. Sackellares. Transition to epileptic seizures: Optimization. In D. Du, P.M. Pardalos, and J. Wang, editors, Discrete Mathematical Problems with Medical Applications, pages 55–74. DIMACS series, Vol. 55, American Mathematical Society, 2000.
M.P. Jacobs, G.D. Fischbach, M.R. Davis, M.A. Dichter, R. Dingledine, D.H. Lowenstein, M.J. Morrell, J.L. Noebels, M.A. Rogawski, S.S. Spencer, and W.H. Theodore. Future directions for epilepsy research. Neurology, 57: 1536–42, 2001.
K. Lehnertz and C.E. Elger. Can epileptic seizures be predicted? Evidence from nonlinear time series analysis of brain electrical activity. Physical Review Letters, 80: 5019–5022, 1998.
B. Litt, R. Esteller, J. Echauz, M. D’Alessandro, R. Short, T. Henry, P. Pennell, C. Epstein, R. Bakay, M. Dichter, and G. Vachtsevanos. Epileptic seizures may begin hours in advance of clinical onset: A report of five patients. Neuron, 30: 51–64, 2001.
J. Martinerie, C. Adam, M. Le Van Quyen, M. Baulac, S. Clemenceau, B. Renault, and F.J. Varela. Epileptic seizures can be anticipated by nonlinear analysis. Nature Medicine, 4: 1173–1176, 1998.
F. Mormann, R.G. Andrzejak, T. Kreuz, C. Rieke, P. David, C.E. Elger, and K. Lehnertz. Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients. Physical Review E, 67: 021912-1–10, 2003.
M.L.V. Quyen, J. Martinerie, V. Navarro, P. Boon, M. Dhave, C. Adam, B. Renault, F. Varela, and M. Baulac. Anticipation of epileptic seizures from standard EEG recordings. Lancet, 357: 183–188, 2001.
M.L.V. Quyen, J. Martinerie, M. Baulac, and F. Varela. Anticipating epileptic seizures in real time by non-linear analysis of similarity between EEG recordings. NeuroReport, 10: 2149–2155, 1999.
J.C. Sackellares, L.D. Iasemidis, P.M. Pardalos, W. Chaovalitwongse, D.S. Shiau, S.N. Roper, R.L. Gilmore, and J.C. Principe. Performance Characteristics of an Automated Seizure Warning Algorithm (ASWA) Utilizing Dynamical Measures of the EEG Signal and Global Optimization Techniques. Epilepsia, 42(S7): 40, 2001.
J.C. Sackellares, L.D. Iasemidis, R.L. Gilmore, and S.N. Roper. Epilepsy-when chaos fails. In K. Lehnertz, J. Arnhold, P. Grassberger, and C.E. Elger, editors, Chaos in the Brain?, pages 112–133. World Scientific, Singapore, 2000.
A.Y. Toledano. Three methods for analyzing correlated ROC curves: A comparison in real data sets from multi-reader, multi-case studies with a factorial design. Statistics in Medicine, 22: 2919–2933, 2003.
D. Vere-Jones. Forecasting earthquakes and earthquake risk. International Journal of Forecasting, 11: 530–538, 1995.
M. Winterhaider, T. Maiwald, H.U. Voss, R. Aschenbrenner-Scheibe, J. Timmer, and A. Schulze-Bonhage. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy and Behavior, 4: 318–325, 2003.
D.D. Zhang, X.H. Zhou, D.H. Freeman, J.L. Freeman. A non-parametric method for comparison of partial areas under ROC curves and its application to large health care data sets. Statistics In Medicine, 21: 701–715, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Shiau, D.S. et al. (2007). Automated Seizure Prediction Algorithm and its Statistical Assessment: A Report from Ten Patients. In: Pardalos, P.M., Boginski, V.L., Vazacopoulos, A. (eds) Data Mining in Biomedicine. Springer Optimization and Its Applications, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-69319-4_26
Download citation
DOI: https://doi.org/10.1007/978-0-387-69319-4_26
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-69318-7
Online ISBN: 978-0-387-69319-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)