Adaptive epileptic seizure prediction system

Abstract

Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure 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 epileptic seizures, 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 seizure 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 seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure 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 patients 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.

Continuous energy variation during the seizure cycle: towards an on-line accumulated energy

“…These suprathreshold outputs are then categorized as either true positives (TPs) or false positives (FPs) based upon whether or not a seizure occurred within that prediction horizon after the prediction was made [10]. The range of prediction horizons was chosen based upon a review of the literature, ranging from 5 min to a maximum of 3 h (Iasemidis et al, 2003;Litt and Echauz, 2002). Positive predictions that occurred during seizures were not considered FPs, because the algorithm considers seizure detection to be a special case of seizure prediction where the prediction horizon is zero, and in addition, ictal EEG corresponds to a different brain situation from our target states that were interictal and pre-ictal EEG.…”

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

“…However, a robust definition of such a state and with it, its possibility to predict an impeding seizure, is a much harder mathematical problem. For the time being, one can assume the existence of a preictal state based on recent investigations that have found physiological and clinical support for the idea that certain types of seizures are predictable, [44,45,46,47,48,49]. Functional mri based evidence of transitional states preceding seizures [8] and interictal spikes [50] can also lend support to this hypothesis.…”

“…. , N , (45) where θ j and ω j denote, respectively, the phase and frequency of oscillator j with the function Γ ij (θ j − θ i ) defining the nature of the coupling occurring between oscillators i and j.…”

An Enhanced Wavelet Neural Network Model with Metaheuristic Harmony Search Algorithm for Epileptic Seizure Prediction

“…Observe that equation (1) can be rewritten in a more compact form Y = GW , where Y is the output, W is the matrix that stores the weight values, and G is defined as follows:…”