Abstracts

Rationale: To achieve high sensitivities, available seizure-prediction algorithms are so far accompanied by high numbers of false predictions. This study aims at understanding potential causes and the circadian distribution of false predictions as well as their relation to the sleep–wake cycle.Methods: In 21 patients, each with at least 24 h of interictal invasive EEG recordings, two methods, the dynamic similarity index and the mean phase coherence, were assessed with respect to time points of false predictions. Visual inspection of the invasive EEG data and additional scalp electroencephalogram data was performed at times of false predictions to identify possible correlates of changes in the EEG dynamics.Results: A circadian dependency of false predictions is shown to be significant for the dynamical similarity index. Renormalized to the duration of the period patients are asleep and awake, 86% of all false predictions occurred during sleep for the dynamic similarity index and 68% for the mean phase coherence, respectively. The dynamic similarity index increases its performance by reducing the number of false predictions by almost 50% without major changes in sensitivity by combining two reference intervals, one during sleep and one in an awake state.Conclusions: A certain amount of false predictions is related to changes in the EEG dynamics which in turn are related to sleep-wake cycles. Utilizing this knowledge may provide a clue for improving prediction methods in general, for instance by adaptations of the paramters of the algorithms corresponding to the time of the day. Moreover, the combination of reference states yields promising results for the dynamic similarity index.