Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

ABSTRACT: There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and con

SUBMITTER: Baldassano SN 

PROVIDER: S-EPMC6075622 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature