On the predictability of epileptic seizures
Introduction
One of the most disabling aspects in epilepsy is the sudden, unforeseen way in which epileptic seizures strike ‘like a bolt from the blue’. Apart from the risk of serious injury, there is often a severe feeling of helplessness that has a strong impact on the everyday life of a patient. It is undisputed that a method capable of predicting the occurrence of seizures would significantly improve the therapeutic possibilities (Elger, 2001) and thereby the quality of life for epilepsy patients. In addition to portable warning systems, automated on-demand application of short-acting drugs as well as electrical stimulation or other intervention strategies could be envisioned.
A question of particular interest is whether apart from clinical prodromi (which are found only in some of the patients (cf. Rajna et al., 1997)) characteristic and objective features can be extracted from the continuous EEG that are predictive of an impending seizure. Much research has been carried out on this topic, and recent studies have reported certain measures derived from the theory of dynamical systems to be to some extent capable of extracting information from the EEG that allow the detection of a preictal state. For an overview of the literature on seizure prediction see Lehnertz and Litt (this issue).
Despite the many publications reporting evidence for the existence of a pre-seizure state, to date no report of a prospective or quasi-prospective prediction of seizures has been published. A major problem with most of the studies presented up to now is that they do not sufficiently (or not at all) investigate the specificity of the described precursors using interictal EEG as control. In addition, many of these studies rely on the use of a posteriori knowledge, e.g. by selecting certain channels out of a large number of channels, or bear the risk of an in-sample over-training of parameters used to calculate measures for the extraction of predictive information. A particular issue that has been neglected by past studies is the need for statistical validation in order to assess the statistical significance of the predictive performance for a given EEG measure (cf. Andrzejak et al., 2003, Mormann et al., 2003b, Winterhalder et al., 2003, Kreuz et al., 2004). Another problem is that up to now no extensive comparison of the performance of different approaches for seizure prediction has been published. Furthermore, there is little experience with continuous long-term-recordings over days, and no study has been reported on comprising different patients from different centers using different pre-surgical evaluation protocols and acquisition systems.
In this paper, we determine the ability of a number of measures to distinguish between the preictal and interictal period using a statistical approach that does not rely on any additional a posteriori knowledge. Retrospectively analyzing continuous multi-day recordings from a group of five patients acquired at different centers, we compare the performance of different univariate and bivariate measures, comprising both linear and non-linear approaches. Applying different smoothing filters to the measures' time profiles and allowing different durations of the preictal period, we evaluate the potential predictive performance of these measures without any antecedent assumptions in the sense of expecting either a preictal increase or a decrease of a measure. We design a number of different evaluation schemes including the analysis of separate channels and seizures and the use of both a constant and an adaptive baseline and pay special attention to issues such as data quality and artifact control as well as statistical validation using seizure time surrogates as a means to assess the statistical significance of an obtained performance value. Based on our results, we discuss the suitability of different measures for seizure prediction.
Section snippets
Patient characteristics and data acquisition
The analyzed recordings were intracranial multi-day recordings from five patients acquired at different epilepsy centers comprising 46 seizures and a total recording time of 311 h. For detailed information on patient characteristics and data acquisition, refer to Lehnertz and Litt (this issue). Since seizure onset times for the different data sets were supplied by the centers providing the data, we used these times although the criteria applied to determine them may have differed among the
Results
The statistical evaluation based on ROC curves is shown in Fig. 1. In this example, the time profile of a measure for phase synchronization (mean phase coherence based on Hilbert Transform RH) for one channel combination of a patient (data set B) is plotted in the upper row of the figure. A smoothing filter of d=5 min was applied and the duration of the preictal period was set to s=240 min in this example. The corresponding amplitude distributions of RH for both the preictal and the interictal
Discussion
When comparing different measures in terms of their suitability for seizure prediction by testing the ability of these measures to discriminate a presumed preictal state from the interictal period, it is useful to distinguish between univariate and bivariate aproaches.
While univariate measures quantify certain properties of the EEG signal thus possibly reflecting the state of a certain region of the brain, the bivariate measures analyzed in this study quantify the degree of synchronization
Acknowledgements
We are grateful to Peter Grassberger and Martin Kurthen for useful discussion and valuable comments. This work was supported by the Deutsche Forschungsgemeinschaft.
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