Adaptive epileptic seizure prediction system

By L. Iasemidis, Deng-Shan Shiau, W. Chaovalitwongse, J. Sackellares, P. Pardalos, José Carlos Príncipe, P. Carney, Awadhesh Prasad, Balaji Veeramani, Konstantinos Tsakalis. IEEE Transactions on Biomedical Engineering, 2003

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A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy (2007, background): “chaos in the brain was reduced in the preictal phase [8], [9].”
Patient-specific seizure onset detection (2004, methodology): “[21] developed an algorithm that predicts seizure onsets by characterizing the level of entrainment observed in spatially related channels using the Lyapunov exponent nonlinear dynamical indicator.”
Seizure prediction: the long and winding road. (2007, methodology): “Several groups have determined false prediction rates by counting all false positives and dividing this number by the total duration of the analysed recording (Iasemidis et al., 2003; Niederhauser et al., 2003; Chaovalitwongse et al., 2005; Esteller et al., 2005; Iasemidis et al., 2005).”
Focal Onset Seizure Prediction Using Convolutional Networks (2018, background): “The advent of the theory of dynamical systems introduced a number of non-linear features, such as the dynamical similarity index [16], largest Lyapunov exponent [17], phase synchronization [18], attractor states [19], and combinations of non-linear features [20].”