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The performance of traditional linear (variance based) methods for the identification and prediction of epileptic seizures are contrasted with "modern" methods from nonlinear time series analysis. We note several flaws of design in demonstrations claiming to establish the efficacy of nonlinear techniques; in particular, we examine published evidence for precursor identification. We perform null hypothesis tests using relevant surrogate data to demonstrate that decreases in the correlation density prior to and during seizure may simply reflect increases in the variance.

Original publication




Journal article


IEEE transactions on bio-medical engineering

Publication Date





628 - 633


Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK.


Brain, Humans, Epilepsy, Temporal Lobe, Seizures, Sclerosis, False Positive Reactions, Electroencephalography, Linear Models, Sensitivity and Specificity, Reproducibility of Results, Electrodes, Implanted, Algorithms, Nonlinear Dynamics, Models, Neurological, Quality Control, Computer Simulation, Signal Processing, Computer-Assisted, Statistics as Topic