A gradient-based adaptive learning framework for online seisure prediction

International Journal of Data Mining and Bioinformatics
Shouyi WangStephen Wong

Abstract

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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