Abstract
Epilepsy is characterized by the sudden occurrence of seizures disturbing the perception or behavior of epileptic patients. Several prediction methods have claimed to be able to predict seizures based on EEG-recordings minutes in advance, which opens up new approaches to treat the disease. However, the term seizure prediction is not unequivocally defined and different assessment criteria for prediction methods exist which impedes the comparison between methods. Moreover, only little attention is paid to the dependency between sensitivity and false prediction rate. We address these shortcomings and introduce a terminology and assessment criterion for seizure prediction methods based on statistical and clinical considerations: the seizure prediction characteristic.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bauer, J. and Burr, W. (2001). Course of chronic focal epilepsy resistant to anticonvulsant treatment. Seizure, 10: 239–46.
De Clercq, W., Lemmerling, P., Van Huffel, S., and Van Paesschen, W. (2002). Evaluation of methods for determining a preictal state in scalp-eeg measurements. Biosignal Processing, 10.
Haut, S. R., Swick, C., Freeman, K., and Spencer, S. (2002). Seizure clustering during epilepsy monitoring. Epilepsia, 43 (7): 711–715.
Iasemidis, L., Pardalos, P., Sackellares, J., and Shiau, D. (2001). Quadratic binary programming and dynamical system approach to determine the predictibilty of epileptic seizures. Journal of Combinatorial Optimization, 5: 9–26.
Jerger, K. K., Netoff, T. I., Francis, J. T., Sauer, T., Pecora, L., Weinstein, S. L., and Schiff, S. J. (2001). Early seizure detection. Journal of Clin. Neurophysiology, 18: 259–268.
Le Van Quyen, M., Adam, C., Martinerie, J., Baulac, M., Clemenceau, S., and Varela, F. (2000). Spatio-temporal characterizations of non-linear changes in intracranial activities prior to human temporal lobe seizures. European Journal of Neuroscience, 12: 2124–2134.
Le Van Quyen, M., Martinerie, J., Baulac,M., and Varela, F. (1999). Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. Neuroreport, 10: 2149–2155.
Le Van Quyen, M., Martinerie, J., Navarro, V., Boon, P., D’Have, M., Adam, C., Renault, B., Varela, F., and Baulac, M. (2001). Anticipation of epileptic seizures from standard EEG recordings. The Lancet, 357: 183–188.
Lehnertz, K., Andrzejak, R., Arnhold, J., Kreuz, T., Mormann, F., Rieke, C., Widman, G., and Elger, C. (2001). Nonlinear EEG analysis in epilepsy: Its possible use for interictal focus localization, seizure anticipation, and prevention. J Clin. Neurophysiol., 18: 209–222.
Lehnertz, K. and Elger, C. (1998). Can epileptic seizures be predicted? Evidence from nonlinear time series analysis of brain electrical activity. Phys. Rev. Lett., 80: 5019–5022.
Litt, B. and Echauz, J. (2002). Prediction of epileptic seizures. The Lancet Neurology, 1: 22–30.
Litt, B. and Lehnertz, K. (2002). Seizure prediction and the preseizure period. Current Opinion in Neurology, 15: 173–177.
Martinerie, J., Adam, C., Quyen, M. L. V., Baulac, M., Clemenceau, S., Renault, B., and Varela, F. (1998). Epileptic seizures can be anticipated by non-linear analysis. Nature Medicine, 4: 1173–1176.
Mormann, F., Andrzejak, R., Kreuz, T., Rieke, C., David, P., Elger, C., and Lehnertz, K. (2003). Automated detection of a pre-seizure state based on a decrease in synchronization in intracranial EEG recordings from epilepsy patient. Phys. Rev. E, 67: 021912.
Navarro, V., Martinerie, J., Le Van Quyen, M., Clemenceau, S., Adam, C., Baulac, M., and Varela, F. (2002). Seizure anticipation in human neocortical partial epilepsy. Brain, 125: 640–655.
Osorio, I., Frei, M., and Wilkinson, S. (1998). Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset. Epilepsia, 39 (6): 615–27.
Schindler, K., Wiest, R., Kollar, M., and Donati, F. (2002). EEG analysis with simulated neuronal cell models helps to detect pre-seizure changes. Clin. Neurophysiol., 113: 604–614.
Winterhalder, M., Maiwald, T., Voss, H., Aschenbrenner-Scheibe, R., Timmer, J., and Schulze-Bonhage, A. (2003a). The seizure prediction characteristic - a general framework to assess and compare seizure prediction methods. Epilepsy and Behavior,4(3):318–325.
Winterhalder, M., Maiwald, T., Voss, H. U., Aschenbrenner-Scheibe, R., Schulze-Bonhage, A., and Timmer, J. (2003b). Seizure prediction methods - assessment and comparison by means of the seizure prediction characteristic. In this volume. Kluwer Academic Publishers.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Kluwer Academic Publishers
About this chapter
Cite this chapter
Maiwald, T., Winterhalder, M., Voss, H.U., Aschenbrenner-Scheibe, R., Schulze-Bonhage, A., Timmer, J. (2004). The Seizure Prediction Characteristic. In: Pardalos, P.M., Sackellares, J.C., Carney, P.R., Iasemidis, L.D. (eds) Quantitative Neuroscience. Biocomputing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0225-4_5
Download citation
DOI: https://doi.org/10.1007/978-1-4613-0225-4_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-7951-5
Online ISBN: 978-1-4613-0225-4
eBook Packages: Springer Book Archive