A gradient-based adaptive learning framework for online seizure prediction
by Shouyi Wang; Wanpracha Art Chaovalitwongse; Stephen Wong
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 10, No. 1, 2014

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.

Online publication date: Tue, 21-Oct-2014

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