Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

ABSTRACT:

Background

Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs.

Methods

Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be pri

SUBMITTER: Kiral-Kornek I 

PROVIDER: S-EPMC5828366 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature