Abstract

A variant of the method of surrogate data is applied to a single time series from an electroencephalogram (EEG) recording of a patient undergoing an epileptic seizure. The time series is a nearly periodic pattern of spike-and-wave complexes. The surrogate data sets are generated by shuffling the individual spike-and-wave cycles, and correspond to a null hypothesis that there is no deterministic structure in the cycle-to-cycle variability of the original data. Using estimates of autocorrelation, correlation dimension, and Lyapunov exponent as discriminating statistics, the evidence for dynamical correlations between successive spike-and-wave patterns is evaluated both formally and informally.

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