Title: A gradient-based adaptive learning framework for online seizure prediction

Authors: Shouyi Wang; Wanpracha Art Chaovalitwongse; Stephen Wong

Addresses: Department of Industrial and Systems Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA ' Department of Industrial and Systems Engineering, Department of Radiology at Medical Center, University of Washington, Seattle, WA 98195, USA ' Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, New Brunswick, NJ 08901, USA

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.

Keywords: adaptive seisure prediction; gradient-based learning; time series data mining; bioinformatics; adaptive learning; online seizure prediction; epileptic seizures; epilepsy; electroencephalograms; EEG patterns; EEG monitoring; personalized seizure predication; personalisation.

DOI: 10.1504/IJDMB.2014.062888

International Journal of Data Mining and Bioinformatics, 2014 Vol.10 No.1, pp.49 - 64

Received: 02 May 2011
Accepted: 02 May 2011
Published online: 21 Oct 2014 *

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