Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that prediction methods are applied to multiple time series and several seizures. Their performance is illustrated utilizing a bivariate seizure prediction method based on synchronization theory. © 2006 American Institute of Physics. ͓DOI: 10.1063/1.2137623͔ Epileptic seizures present a nonlinear dynamic phenomenon. Seizures are generated by an abnormal synchronization of neurons which is unforeseeable for the patients. A reliable and timely prediction would considerably increase the quality of life of those patients who cannot be treated successfully by common therapeutic strategies. Analysis techniques from the theory of nonlinear dynamics could thus make an important contribution to the medical treatment of a severe neurological disease if a reliable prediction was guaranteed. But the lack of a widely accepted methodology to evaluate and compare seizure prediction algorithms has so far led to contradicting claims about their performance and hampered their practical application. Here, we present a methodology to assess seizure prediction performance and a statistical test to judge its superiority to a random predictor. This methodology is then demonstrated in an application to invasive electroencephalographic recordings using a quantity measuring phase synchronization.
Statistical methods, such as multivariate time series analysis techniques REF , attempt to estimate the usefulness of extracted features to the prediction problem retrospectively.
Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction.
Chaos
Chaos
Computer Science Medicine