Abstract
The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure – so-called measure profiles – for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating process.
Highlights
Epilepsy affects 60 million humans worldwide, which is approximately 1% of the world’s population (Hauser et al, 1996)
Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics
For Monte-Carlo based approaches, which use randomized surrogate data to test for statistical significance (Andrzejak et al, 2003, 2009; Kreuz et al, 2004), the high number of measure profiles may lead to non-independent surrogate realizations. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states and to identify brain regions associated with seizure precursor dynamics, by exploiting specific aspects that appear to be associated with seizure precursor dynamics
Summary
Epilepsy affects 60 million humans worldwide, which is approximately 1% of the world’s population (Hauser et al, 1996). The sudden, apparently unforeseen occurrence of seizures, which represents one of the most disabling aspects of epilepsy (Murray, 1993; Schulze-Bonhage et al, 2010), calls for a method that is capable of predicting the occurrence of seizures. This could significantly advance therapeutic possibilities (Elger, 2001). For those patients who can be treated successfully nowadays, current preventive treatment strategies could be replaced by an on-demand therapy. This includes, among others, the application of fast-acting anticonvulsant substances or electrical stimulation (Theodore and Fisher, 2004; Morrell, 2006; Stacey and Litt, 2008)
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