Adaptive XXXX prediction system.

@entity1 @entity410 @entity1315 @entity302
Current @entity302 "prediction" algorithms are generally based on the knowledge of @entity410 occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to @entity302 , they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first @entity410 is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending @entity410 is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive @entity410 prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five @entity1 with refractory @entity1315 . A fixed parameter setting applied to all cases predicted 82% of @entity410 with a false prediction rate of 0.16/h. @entity410 warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual @entity1 improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.
