title: A gradient-based adaptive learning framework for online seisure prediction.
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.
authors.forename: Shouyi
authors.initials: S
authors.lastname: Wang
authors.forename: Wanpracha Art
authors.initials: WA
authors.lastname: Chaovalitwongse
authors.forename: Stephen
authors.initials: S
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D057225:Data Mining / Q000379:methods*; D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014584:User-Computer Interface