A gradient-based adaptive learning framework for online seizure prediction

Most of the current epileptic seizure prediction algorithms require much prior knowledge of a patient's pre-seizure electroencephalogram (EEG) patterns. They are impractical to be applied to a wide range of patients due to a high inter-individual variability of pre-seizure EEG patterns. This paper proposes an adaptive prediction framework, which is capable of accumulating knowledge of pre-seizure EEG patterns by monitoring long-term EEG recordings. The experimental results on five patients indicate that the adaptive prediction framework is effective to improve prediction accuracy over time and thus achieve a personalized seizure predication for each patient.

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